LIST OF TAGS
- agile14
- algorithms31
- analytics36
- big data358
- computer graphics71
- computer networks16
- data curation13
- data management287
- data science39
- design patterns14
- developments78
- foundation models1
- gpgpu23
- graph mining501
- hadoop & mapreduce168
- htap39
- machine learning1173
- nosql24
- papers36
- parallel computing92
- presentation82
- publications12
- software engineering19
- spark59
- sql on hadoop17
- stream computing48
- tez30
- yarn52
agile
- » Unit Test
- » Burndown Chart
- » Wiki Tutorial
- » Adapting to Change
- » Agile Big Data
- » TDD Best practices
- » Test-Driven Development in a nutshell
- » Scrum Events
- » Effective Team Membership
- » JUnit, A Tool for Test-Driven Development
- » Explaining Agile
- » Extreme Programming
- » Scrum Agile Development Process
- » Agile Processes and Scrum
algorithms
- » Notes on Graph Theory
- » The Robot Revolution - The New Age of Manufacturing
- » Evolutionary Generative Adversarial Networks
- » Time Series Generation with Recurrent Conditional GANs
- » Predicting Car Prices using Neural Networks
- » Predicting Car Prices - Linear Regression
- » Deep Reinforcement Learning
- » Brains, Minds and Machines Summer Course
- » OpenAI Meta Learning and Self Play
- » Sequential Implementation of Mesoscopic Simulation
- » Predicting congestion on London's roads with TensorFlow
- » Improving Traffic Prediction Using Weather Data
- » Limitations of Macroscopic and Mesoscopic Simulation
- » Simulation Loop
- » Mesoscopic Simulation
- » Distributed Systems
- » High-Performance Mesoscopic Traffic Simulation
- » Microscopic Simulation
- » Macroscopic Simulation
- » Traffic Simulation Background
- » T tree
- » Cellular Automata
- » Graph Partitioning
- » Computability, Complexity, and Theory - Georgia Tech
- » PageRank Algorithm
- » PageRank History
- » Bloom Filter
- » Confluent and Functional Persistence
- » Full Persistence
- » Partial Persistence
- » Persistent Data Structures
analytics
- » Graphs in Machine Learning
- » Social and Information Network Analysis
- » Structural Analysis and Visualization of Networks
- » Perform SQL Selects on R Data Frames
- » R Graphics
- » Google's R Style Guide
- » IBM Watson at Work
- » Hype Cycle
- » Predictive Methodologies - Analysis of Time and Space
- » Predictive Methodologies - Analysis of Space
- » Predictive Methodologies - Analysis of Social Networks
- » Theoretical Foundations of Data Mining and Predictive Analytics
- » Data Used in Predictive Policing
- » Operational Challenges of Predictive Policing
- » Forecast Vs. Prediction
- » Recommendations
- » Predictive Policing Myths and Pitfalls
- » Prediction-Led Policing Process and Prevention Methods
- » Predictive Policing
- » A Taxonomy of Predictive Methods
- » “Big Data” Promises a Revolution
- » T-Shaped Data Scientists
- » Adapting to Change
- » Agile Big Data
- » Scales of Big Data
- » The Variety of Data Scientists
- » The Real-Time Big Data Architecture (RTDBA) Stack
- » The Five Phases of Real Time
- » Case Studies of Analyzing Data
- » Data Science, Engineering, and Data-Driven Decision Making
- » Clustering Data Scientists
- » Solving the Wanamaker Problem for Healthcare
- » Parallel R
- » Real-time Operational Intelligence
- » Analytics vs. Predictive Analytics
- » The evolution of analytics
big data
- » What's the difference between programming and coding
- » Prompt Engineering Overview
- » Cross-Platform Data Lineage with OpenLineage
- » Continuous Training and Deployment with ZenML and Seldon
- » CVPR Highlights 2023
- » Robust and Trustworthy Deep Learning
- » PFNs; Use neural networks for 100x faster Bayesian predictions
- » Multimodal Deep Learning for Protein Engineering
- » Topological Deep Learning; Going Beyond Graph Data
- » Is Distance Matrix Enough for Geometric Deep Learning?
- » Sign and Basis Invariant Networks for Spectral Graph Representation Learning
- » Distributed and Multiagent Reinforcement Learning
- » A Distributional Multi-agent Reinforcement Learning Approach
- » Deploy and Scale Models with BentoML
- » Tool Agnostic MLOps with ZenML
- » The Four Pillars of Machine Learning
- » When to use Kubernetes natively over Kubeflow for ML
- » Deploy ZenML + Kubeflow + MLflow + Minio
- » MLOps Pipeline Tutorial with ZenML and Kubeflow
- » ML models with ZenML and BentoML
- » Recent Advances in Vision Foundation Models
- » CVPR 2023 Tutorials
- » Model Registry and Deployment with MLflow
- » How to Deploy ML Models in Production with BentoML
- » Data Science–A Systematic Treatment
- » LEARN ENGLISH with STEVE JOBS
- » DuckDB Internals
- » How to Master the Art of Leadership
- » Getting Started with ZenML
- » Generative Models for Language and Vision
- » ChatGPT, LLMs & Generative AI; What Your Business Needs to Know
- » Differential Privacy & Variants
- » Towards Learned Database Systems
- » Deep Lake; a Lakehouse for Deep Learning
- » Towards Natural Language Query Answering
- » Do We Still Need People To Write Database Systems?
- » Generative AI and Databases
- » Contrastive Learning in PyTorch
- » Self-Supervised Learning; Self-Prediction and Contrastive Learning
- » Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
- » Bayesian Neural Network
- » Study Less Study Smart
- » Kubeflow v1.7 Release Overview
- » Amazon Redshift Internals
- » ChatGPT Course – Use The OpenAI API to Code 5 Projects
- » Installing and Using NVIDIA Docker
- » Hypertree Decompositions
- » What is Platform Engineering?
- » Databricks Spark SQL / Photon
- » Google BigQuery / Dremel
- » Personalized Image Generation
- » Kubeflow Pipelines 2.0
- » Learning to Transfer Knowledge Through Embedding Spaces
- » Traffic Prediction with Transfer Learning
- » First-Passage Percolation and Related Models
- » Sparse Fourier Transform Algorithm for Real-Time Applications
- » The Frontier of Deep Learning for Robotics
- » Microsoft's Products Will Soon Access Open AI Tools Like ChatGPT
- » LLaMA & Alpaca; "ChatGPT" On Your Local Computer
- » Multi-Objective Recommender Systems
- » Hands-on Explainable Recommender Systems with Knowledge Graphs
- » Improving Recommender Systems with Human in the Loop
- » Open-Source Systems for Federated Learning
- » An Introduction to Federated Computation
- » Data Governance and Sharing on Lakehouse
- » Data Mesh and Lakehouse
- » Master Data Management
- » Setting up Big Data Fabric
- » On Future of the Modern Data Stack
- » Data Virtualization in Data Fabric
- » Recommender Systems using Graph Neural Networks
- » Activate Your Modern MetaData Stack
- » Databricks Lakehouse makes payments ingestion and analytics simple
- » The Essential Guide to Data Lineage
- » Data Governance Explained in 5 Minutes
- » Automated Data Lineage with Unity Catalog
- » Active Metadata - Understanding the magic behind the Data Fabric
- » Introduction to Data Mesh
- » PostgreSQL vs MySQL
- » Activate Your Metadata to Empower Innovation
- » Accelerating Hybrid Data Mesh Implementation
- » Deep-Dive into Delta Lake
- » Delta Lake 2.0 Overview
- » Advancing Spark - The Photon Whitepaper
- » Column-Level Lineage and Active Metadata
- » Five Things to Consider About Data Mesh and Data Governance
- » Data Mesh Implementation Patterns
- » Intro to Databricks Lakehouse Platform Architecture and Security
- » Meshing About with Databricks
- » Enterprise Data Fabric
- » Data Fabric for Self-Driving Cars
- » Speed at Scale - Using GPUs to Accelerate Analytics for Extreme Use Cases
- » SpatialHadoop - A MapReduce Framework for Big Spatial Data
- » Mining Massive Data Sets
- » Latent Space Model for Road Networks to Predict Time-Varying Traffic
- » Three Applications of Deep Learning in Big Data Analytics
- » Fog Computing vs. Edge Computing
- » From Big to Now - The Changing Face of Data
- » Fast Data at Internet Scale with Amazon ElastiCache for Redis
- » Fast Data, Fast Monitoring
- » Wide & Deep Learning; Memorization + Generalization with TensorFlow
- » Keynote - TensorFlow Dev Summit 2017
- » Cloud of Things Overview
- » Big Data and Smart Data
- » 5 Ways to Make Your Hive Queries Run Faster
- » TensorFlow Introduction
- » Apache Ignite In-Memory Data Fabric
- » Introduction to HDFS Erasure Coding in Apache Hadoop
- » SDN and Big Data
- » Connected Components in MapReduce and Beyond
- » Analytics in Motion (AIM)
- » Big Data is (at least) Four Different Problems
- » Real-time Big Data Analytics
- » HiBench Suite
- » Map-D
- » MultiQx-GPU
- » Concurrent Analytical Query Processing with GPUs
- » Where are the GPU based SQL databases?
- » Comparing Relational Databases, Memory Cache, and NoSQL
- » Oracle In-Memory Database Cache Overview
- » MapGraph
- » Introducing TPCx-HS Benchmark for Big Data
- » Big Data, Fast Data The Need for In Memory Database Technology
- » Arrary DBMS
- » Arrary Databases
- » Michael Stonebraker's Talk
- » Sample Data
- » Building Tachyon Master Branch
- » Creating Applications using Spark Streaming
- » Creating a Spark Applications
- » Working with RDD Operations
- » RAMCloud Presentations
- » Teaching creative computer science
- » RAMCloud Training
- » Kudu - New Apache Hadoop Storage for Fast Analytics on Fast Data
- » GridGain In-Memory Data Fabric
- » Discardable Memory and Materialized Queries
- » 2014 ACM A.M. Turing Lecture
- » What is the Future of Hadoop?
- » Sparrow Paper Review
- » Spark Architecture Shuffle
- » Explore In-Memory Data Store Tachyon
- » Tachyon Overview
- » Perform SQL Selects on R Data Frames
- » R Graphics
- » Google's R Style Guide
- » Approximate Aggregation Queries in Presto
- » Off-heap Memory in Apache Flink and the curious JIT compiler
- » AMP Camp 6
- » Spark 1.5 DataFrame API Highlights
- » Fundamentals of Parallel Computing with the GPU
- » Text Mining and Knowledge Graphs in the Cloud
- » Knowledge Graph and Cognitive Computing
- » Using Presto to combine data from Hive and MySQL
- » IBM Watson at Work
- » Using HyPer with PostgreSQL Drivers
- » Knowledge Graph
- » Big Data and Machine Learning - Keys to AI Progress
- » Distributed Computing Conferences
- » Building Good Docker Images
- » Cognitive Computing
- » Using Electronic Health Records for Better Care
- » 6 Big Data Analytics Use Cases for Healthcare IT
- » Four Use Cases for Healthcare Predictive Analytics, Big Data
- » Five open source Big Data projects to watch
- » Reservoir Sampling in MapReduce
- » Clinical Data Repository Versus a Data Warehouse
- » Top 50 Data Science Resources
- » High-throughput, low-latency, and exactly-once stream processing with Apache Flink
- » Hyper
- » From Big Data to Fast Data
- » SAP Hana In-Memory Explained In Nine Minutes
- » Business unIntelligence
- » The Challenges of Data Science
- » How Data Science Driven Software is Eating the Connected World
- » Data Curation as Publishing for the Digital Humanities
- » How WellCare Accelerated Big Data Delivery to Improve Analytics
- » Sharing Experiences in Cloud Adoption
- » IBM Hybrid Transaction Analytic Processing HTAP
- » What is Hybrid Transaction/Analytical Processing (HTAP)?
- » CIOs and the Ongoing Data Persistence Struggle
- » Eight Big Trends in Big Data Analytics
- » Hybrid Trasansaction/Analytical Processing (HTAP)
- » Hybrid Trasansactional/Analytical Processing (HTAP)
- » HTAP and The Future of Data
- » Does ethical content curation exist? A data-driven answer
- » Stonebraker points to data curation as next integration step
- » Three Approaches to Scalable Data Curation
- » The Landscape of Big Data, Curation
- » Data Curation
- » Tackling Data Curation
- » Hype Cycle
- » The Virginian Database
- » The Analytical Bootstrap
- » DAQ Introduction
- » A New Paradigm for Approximate Query Processing
- » Future of big data analytics
- » Five most popular similarity measures implementation in python
- » Overview of the Apache Flink
- » Apache Flink Seminar
- » MapD (Massively Parallel Database) Overview
- » Data Science using GPUs
- » Apache Spark - Caching and Checkpointing Under the Hood
- » Tensor Decomposition
- » Higher-order Singular Value Decomposition
- » Smoothed Analysis of Tensor Decompositions
- » Spark for Unconventional Cores
- » Aparapi for Unconventional Cores
- » Apache Phoenix
- » Immutability
- » Parallelism
- » Column Orientation
- » Composing and Scaling Data Platforms
- » Elements of Scale
- » What is Mesos?
- » Data Governance Initiative
- » 39 Data Visualization Tools for Big Data
- » Twitter Scalding
- » Apache Zeppelin
- » Transaction Processing on Confidential Data using Cipherbase
- » Introduction to the Spark
- » Big Spatial Data Processing using Spark
- » SpatialSpark
- » New Directions for Spark in 2015
- » The Spark SQL Optimizer and External Data Sources API
- » DataFrame and SQL Operations
- » Design Patterns for using foreachRDD
- » Output Operations on DStreams
- » Transformations on DStreams
- » DStream Sources
- » Discretized Streams
- » Spark Streaming Basic Concepts
- » Spark Streaming Quick Example
- » Spark Streaming Overview
- » Useful Research Related to Apache Drill
- » Apache Spark
- » 10 ways to query Hadoop with SQL
- » Analytic Processing Environment (APE)
- » Analytics vs. OLTP
- » Layers of Query Processing
- » Database Workloads
- » Workload Types
- » Detailed Characteristics of an Analytic Workload
- » Characteristics of an Analytic Workload
- » Workload Management for Big Data Analytics
- » SIX sparkling features of Apache Spark!
- » Use Parquet with Impala, Hive, Pig, and MapReduce
- » Actian Unleashes SQL Users on Hadoop with Easy Access, Faster Performance
- » Presto as a Service
- » HIVE as a Service
- » Understanding the world of SQL on Hadoop.
- » Serialization
- » HIVE TPC-DS Benchmark
- » Distributed Computing with Spark
- » Why Oracle Big Data SQL Potentially Solves a Big Issue with Hadoop Security
- » Apache Tez Presentation
- » Announcing Apache Pig 0.13.0
- » Benchmarking Apache Hive 13 for Enterprise Hadoop
- » In-Stream Big Data Processing
- » Apache Hadoop YARN ResourceManager
- » Introduction To Pivotal's Next Generation Analytic Data Lake
- » War of the Hadoop SQL engines. And the winner is …?
- » Exploring Pivotal HD Single Node (VM)
- » How Pivotal Works
- » Parquet - Columnar Storage for the People
- » Pivotal Big Data Suite
- » Pivotal HD Introduction & Demo
- » Special "Use Cases" Big Data & Brews - Hortonworks, Pivotal, MapR
- » What is Apache Drill?
- » Drilling into Big Data with Apache Drill
- » Fun with TPC-H (Part 1)
- » Implementing Drill Math Functions
- » Presto By Facebook – A SQL Engine
- » Big Data - The Hadoop Data Warehouse
- » Building a Data Warehouse with Hadoop
- » Datawarehouse
- » Big Data Architecture
- » Comparing Information Architecture Operational Paradigms
- » Big Data Architecture Capabilities
- » What is SQL-on-Hadoop?
- » Apache Storm Basics
- » What does “100 times faster than Hive” actually mean?
- » GraphX Programming Guide
- » TPC Benchmark
- » M7 - Native Storage for MapR Tables
- » Hadoop 2.4.0 Installation on Mac OS X Mavericks
- » A Leap Forward With SQL on Hadoop
- » Special "How it Works" Big Data & Brews
- » 10 undeniable reasons people hate Big Data
- » 5 technologies that will help big data cross the chasm
- » Big Data & Brews - Ari Zilka, CTO of Hortonworks' Talk
- » NewSQL Vs. SQL on Hadoop
- » Presto as a Service
- » Adding ACID to Apache Hive
- » Eight SQL on Hadoop Challenges
- » 11 SQL-on-Hadoop Tools
- » Processing streaming data in Hadoop with Apache Storm
- » Interactive Query for Hadoop with Apache Hive on Apache Tez
- » Introducing Apache Tez 0.4
- » Big Data Genomics Sequencing
- » Smart Things vs. Many Experienced Things
- » Hardening Hadoop for Healthcare with Project Rhino
- » Functional Programming
- » Why Functional Programming?
- » Implementing Graph-Based Applications
- » Designing Graph-Based Applications
- » Data-intensive Computing
- » Ensuring the Validity, Veracity, and Volatility of Big Data
- » Operationalizing Big Data
- » Modern Oil & Gas Architectures Built with Hadoop
- » Visualization for Data-Intensive Science
- » Stanford Network Analysis Platform(SNAP)
- » Gray’s Laws Database-centric Computing in Science
- » Connecting to the Scientists
- » Big Data Dilemma
- » Polyglot Persistence
- » Predictive Methodologies - Analysis of Time and Space
- » Predictive Methodologies - Analysis of Space
- » Predictive Methodologies - Analysis of Social Networks
- » Theoretical Foundations of Data Mining and Predictive Analytics
- » Data Used in Predictive Policing
- » Operational Challenges of Predictive Policing
- » Forecast Vs. Prediction
- » Recommendations
- » Predictive Policing Myths and Pitfalls
- » Prediction-Led Policing Process and Prevention Methods
- » Facebook Presto
- » Predictive Policing
- » A Taxonomy of Predictive Methods
- » “Big Data” Promises a Revolution
- » T-Shaped Data Scientists
- » Adapting to Change
- » Agile Big Data
- » Scales of Big Data
- » The Variety of Data Scientists
- » The Real-Time Big Data Architecture (RTDBA) Stack
- » The Five Phases of Real Time
- » Case Studies of Analyzing Data
- » Data Science, Engineering, and Data-Driven Decision Making
- » Clustering Data Scientists
- » PredictionIO
- » Multi-tenant Database Systems
- » Big Data in Motion
- » Building the Health Information Infrastructure for the Modern ePatient
- » Solving the Wanamaker Problem for Healthcare
- » Understanding the Types of Big Data Problems
- » The Invention of Stream Computing
- » The Emergence of NoSQL
- » Modelling in Big Data
- » Analyzing Data in Motion A Real-World View
- » Understanding trade-offs with Brewer’s CAP theorem
- » Analytics vs. Predictive Analytics
- » Streams design patterns
- » The evolution of analytics
- » Impedance Mismatch
- » The Value of Relational Databases
- » What is NoSQL?
- » NoSQL History
computer graphics
- » NVIDIA Keynote at SIGGRAPH 2023
- » Hybrid Grains
- » Beautiful Layered Materials, Instantly
- » Gaussian Material Synthesis
- » Self-Illuminating Explosions
- » An Efficient Scattering Material Representation
- » Leveraging Activation Sparsity for Training Deep Neural Networks
- » Deep Photo Style Transfer
- » AI Creates 3D Models From Faces
- » A modular GPU raytracer using OpenCL for non-interactive graphics
- » Semantic Scene Completion from a Single Depth Image
- » Real-Time Oil Painting on Mobile Hardware
- » Geometric Detail Transfer
- » Space-Time Video Completion
- » AI Makes Stunning Photos From Your Drawings
- » Real-time Fiber-level Cloth Rendering
- » Shape and Material from Video
- » Learning to Fill Holes in Images
- » Calendar of Computer Graphics Events
- » AI Builds 3D Models From Images With a Twist
- » How Do Hollywood Movies Render Smoke?
- » Fast Photorealistic Fur and Hair With Cone Tracing
- » Enhance Super Resolution From Google
- » Large-Scale Fluid Simulations On Your Graphics Card
- » AI Makes 3D Models From Photos
- » Text Style Transfer
- » Amazing Slow Motion Videos With Optical Flow
- » Neural Network Learns The Physics of Fluids and Smoke
- » Stunning Video Game Graphics With Voxel Cone Tracing
- » Papers that have used pbrt
- » Mathematical Methods for Robotics, Vision, and Graphics
- » Image Synthesis From Text With Deep Learning
- » PBRT Installation on macOS
- » Water Wave Simulation with Dispersion Kernels
- » Crumpling Sound Synthesis
- » Introduction to "Physically Based Shading in Theory and Practice"
- » Multiphase Fluid Simulations
- » Sound Propagation With Bidirectional Path Tracing
- » 3D Printing Acoustic Filters
- » SIGGRAPH 2016 Papers on the Web
- » Modeling Colliding and Merging Fluids
- » Surface-Only Liquids
- » Artistic Style Transfer For Videos
- » SIGGRAPH 2016 – Technical Papers Trailer
- » Separable Subsurface Scattering
- » Real-Time Shading With Area Light Sources
- » Face2Face; Real-Time Facial Reenactment
- » Embree; High Performance Ray Tracing Kernels
- » Narrow Band Liquid Simulations
- » Photos @ EDBT 2016
- » 3D Depth From a Single Photograph
- » Research Papers in Computer Graphics
- » Readings in Physically Based Rendering
- » Metropolis Light Transport
- » Data Visualization
- » Building LuxRender on OS X
- » Calendar of Computer Graphics Events
- » SIGGRAPH 2015 papers on the web
- » SIGGRAPH 2015 - Technical Papers Trailer
- » 39 Data Visualization Tools for Big Data
- » Keep Your Crises Small
- » Creativity, Inc. Interview
- » SIGGRAPH 2014 Technical Papers
- » SIGGRAPH 2014 Papers
- » Physically Based Shading in Theory and Practice
- » The Forgotten History Of CGI
- » Steve Jobs at SIGGRAPH 1995
- » Visualization for Data-Intensive Science
- » A Material Point Method For Snow Simulation
- » Reflections on Image-Based Modeling and Rendering
- » Korean Computer Graphics Conference 2013
computer networks
- » SDN and Big Data
- » Central Office Re-architected as a Datacenter (CORD)
- » Basic ONOS Tutorial
- » Potential Novel Applications of Open SDN
- » Hybrid Unified Communications and Collaboration
- » SDN Note
- » RAMCloud Overview
- » RAMCloud
- » Open Network Operating System
- » Understanding the Differences between SDN and Server Virtualization
- » Open Daylight Technical Overview
- » An Introduction To Software Defined Networking
- » SDN과 NFV와 네트워크 가상화의 차이점 이해하기
- » How to prepare for SDN
- » Software-Defined Networks
- » Future of the Network Documentary
data curation
- » Knowledge Curation and Knowledge Fusion
- » Self-Curating Databases
- » Machine Driven, Human Guided Data Curation for Decision Advantage and Action
- » Machine Learning as the Key to Personalized Curation
- » Text Mining and Knowledge Graphs in the Cloud
- » Knowledge Graph and Cognitive Computing
- » Knowledge Graph
- » Data Curation as Publishing for the Digital Humanities
- » Does ethical content curation exist? A data-driven answer
- » Stonebraker points to data curation as next integration step
- » Three Approaches to Scalable Data Curation
- » The Landscape of Big Data, Curation
- » Data Curation
data management
- » Awesome Vector Database
- » How to Write a Great Research Paper
- » Vision Transformer - Keras Code Examples
- » Vision Transformer
- » MLP-Mixer; An all-MLP Architecture for Vision
- » From AlphaGo to MuZero
- » Moral Machines; How culture changes values?
- » Generative Adversarial Networks
- » Hardness of Approximation Between P and NP
- » GPT-3 = Generation Pre-trained Transformer 3
- » Graph Attention Networks Implementation
- » Introduction to Pytorch Geometric
- » Deep Learning for Graphs
- » Geometric Deep Learning
- » Graph Neural Networks - Models and Applications
- » Matching of Matching Graphs
- » Graph Classification using Structural Attention
- » Approximate Personalized PageRank on Dynamic Graphs
- » GCNs for Web-Scale Recommender Systems
- » Inductive Representation Learning on Large Graphs
- » Write Research Paper Outline
- » How to Respond to Reviewer Comments
- » Limitations of Graph Neural Networks
- » Choice of Graph Representation
- » Inductive Representation Learning on Temporal Graphs
- » Applications of Graph ML
- » Machine Learning with Graphs
- » How to Review a Research Paper
- » Approximate standing queries on Stream Processing
- » Learning Mesh-Based Simulation with Graph Networks
- » Temporal Graph Networks for Deep Learning on Dynamic Graphs
- » Graph Neural Networks and Knowledge Graph Completion
- » Parallel and Distributed Computing in Python with Dask
- » Reducing Communication in Graph Neural Network Training
- » Mathematical Foundations of the GraphBLAS and Big Data
- » Graph Signal Processing - Accounting for Geometry in Data
- » Graph Signal Processing for Machine Learning Applications
- » Papers on Recommendation Systems
- » Representational Power of Graph Neural Networks
- » Tunable Stream Graph Embeddings at Scale
- » Graph Neural Networks
- » Graph Convolutional Networks using only NumPy
- » Temporal Graph Networks for Dynamic Graphs
- » Graph Algorithms as Matrix Vector Products
- » Distributed Machine Learning with Python
- » Deep Networks Are Kernel Machines
- » How the Quest for the Ultimate Learning Machine Will Remake Our World
- » Fast Distributed Algorithms for Girth, Cycles and Small Subgraphs
- » Graph Sketching, Streaming, and Sampling
- » Theoretical Foundations of Graph Neural Networks
- » Efficiently Answering Span Reachability Queries
- » Probabilistic Reachability for MDPs
- » How computer science can help fight COVID-19
- » Enable Digital Transformation with Big Spatial Data & Analytics
- » Online Approximation Techniques for Spatial Data
- » Sparse Matrices Beyond Solvers
- » New Machine Learning Tool Tracks Urban Traffic Congestion
- » Graph Convolutional Neural Networks for Molecule Generation
- » Deep Learning on Graphs
- » Principles and Applications of Relational Inductive Biases
- » Traffic Prediction with Advanced Graph Neural Networks
- » Papers on Graph Neural Networks
- » Self-Driving Databases
- » Non-Volatile Memory for Database Management Systems
- » Using Apache Arrow, Calcite and Parquet to build a Relational Cache
- » Data Vocalization
- » Cloud of Things Overview
- » Blockchain Explained
- » Proceedings of the VLDB Endowment
- » Data Preparation for Machine Learning
- » How to Prepare Data For Machine Learning
- » IoT, Azure, Machine Learning and more
- » IBM Watson IoT Platform
- » Revisiting Reuse in Main Memory Database Systems
- » What's New with NewSQL?
- » Spark-GPU; An Accelerated In-Memory Data Processing Engine on Clusters
- » GPU SQL Query Accelerator
- » Multi-core Parallelism in a Column-store
- » Self-Driving Database Management Systems
- » Graph Mining for Log Data
- » Understanding Multitenancy and the Architecture of the Salesforce Platform
- » Probabilistic Ranking Problem
- » Data Blocks; Hybrid OLTP and OLAP on Compressed Storage using both Vectorization and Compilation
- » PVLDB Papers
- » Progressive skyline computation in database systems
- » Proceedings of the VLDB Endowment, Volume 10
- » Vectorized Executor
- » Skyline Operator
- » Top 10 Strategic Technology Trends 2016
- » Faster In Memory Computing with No Deadlocks
- » The Emergence of Converged Data Platforms and the Role of In Memory Computing
- » Parallel Data Processing
- » Apache Ignite In-Memory Data Fabric
- » Smart Data Discovery
- » Introduction to LLVM
- » Min-cost Flow Network Algorithms
- » Elasticity with YARN
- » Breakthrough Processor and System Design with SPARC M7
- » Life Beyond Column-Stores - Exploiting intra-cycle parallelism
- » Knowledge Curation and Knowledge Fusion
- » OLTPBench
- » CrowdSky; Skyline Computation with Crowdsourcing
- » Automatic Source Transformation from C++ to CUDA using Clang/LLVM
- » Efficient Computation of Containment and Complementarity in RDF Data Cubess
- » Data Processing in Modern Hardware
- » Self-Curating Databases
- » Verification of Evolving Graph-structured Data under Expressive Path Constraints
- » Top-k Indexes Made Small and Sweet
- » Dynamic Graph Queries
- » Worst-Case Optimal Algorithms for Parallel Query Processing
- » Declarative Probabilistic Programming with Datalog
- » Lightning Fast Social Media Analytics
- » Statistics-Driven OLAP Acceleration using Query Column Sets
- » Concurrency Control and Recovery for Search Trees
- » How DeepMind Conquered Go With Deep Learning
- » 자유가 없는 나라의 공부
- » Skip List
- » Transactional Memory in Practice
- » DB2 for z/OS Best Practice; Locks and Latches
- » Database Recovery - Basic Concepts
- » Latches
- » Apache Calcite Overview
- » A Better B-Tree in Hekaton
- » The Bw-Tree; A B-tree for New Hardware
- » Hekaton Research
- » Distributed Transactions
- » ACID Isolation Level
- » Notes About Memory Allocation Redesign
- » Postgres Modifications
- » The Databaseology Lectures
- » C++ Arena Allocation Guide
- » Region-based Memory Management
- » PostgreSQL
- » What is VoltDB?
- » Release Notes - Tajo - Version 0.11.1
- » Peloton Architecture
- » Vagrant Tutorial
- » Introduction to Vagrant
- » Insert-Only Implementation Strategies
- » A Hands-free Adaptive Store
- » The Bw-Tree Key-Value Store
- » Bitmap Indices
- » IBM Cognos Analytics
- » Understanding the SQL Server Columnstore Index
- » Main Memory Databases
- » Dictionary-based Order-preserving String Compression
- » Hekaton Breaks Through
- » The Merits of Data Compression
- » DBMS Components
- » Compensating Transaction
- » In-Memory Column Store Architecture Overview
- » Advantages & Disadvantages of an In-Memory Database
- » Latches, Spinlocks, and Lock Free Data Structures
- » Research Topics in Database Management
- » Main Memory DBMS
- » HYBRID WORKLOADS AND HTAP
- » Peloton
- » Database Systems Course
- » Gartner Top 10 Tech Trends for 2016
- » CES 2016 Keynote
- » MVCC Overview
- » SAP HANA
- » HYRISE
- » Main Compensation and Delta Compensation
- » Aggregate Cache
- » MonetDB Source compilation on OS X
- » In-Memory Accelerator for Hadoop
- » Main Memory and Streaming Databases
- » Building Postgresql on OS X
- » TPC-H
- » Setting Up An Organized Folder Structure
- » TPC-DS
- » Proceedings of the VLDB Endowment, Volume 9
- » In-Memory Data Management Course
- » Machine Driven, Human Guided Data Curation for Decision Advantage and Action
- » A GPU Parallelized Database Accelerator
- » Databaseology Lectures
- » Zeppelin Deployment
- » Zeppelin Structure
- » The Pushdown of Everything
- » Zeppelin Installation
- » MySQL Installation on Ubuntu
- » PVLDB 2015 Vol. 9
- » Data Engineering Course
- » Apache Spark vs. MapReduce
- » Korean Constitution
- » Database Reading List
- » Big Data, Fast Data The Need for In Memory Database Technology
- » Arrary DBMS
- » Arrary Databases
- » Introduction to LLVM
- » MySQL Installation on Mac OS X
- » Sample Data
- » RAMCloud Presentations
- » Teaching creative computer science
- » Discardable Memory and Materialized Queries
- » 2014 ACM A.M. Turing Lecture
- » IEEE Transactions on Knowledge and Data Engineering
- » Optimizing Big Data Through Curation
- » Cognitive Computing
- » SAP S4 HANA Technical White Board
- » Using Electronic Health Records for Better Care
- » 6 Big Data Analytics Use Cases for Healthcare IT
- » Four Use Cases for Healthcare Predictive Analytics, Big Data
- » Five open source Big Data projects to watch
- » Reservoir Sampling in MapReduce
- » Clinical Data Repository Versus a Data Warehouse
- » Top 50 Data Science Resources
- » High-throughput, low-latency, and exactly-once stream processing with Apache Flink
- » Hyper
- » From Big Data to Fast Data
- » SAP Hana In-Memory Explained In Nine Minutes
- » Business unIntelligence
- » The Challenges of Data Science
- » How Data Science Driven Software is Eating the Connected World
- » Data Curation as Publishing for the Digital Humanities
- » How WellCare Accelerated Big Data Delivery to Improve Analytics
- » Sharing Experiences in Cloud Adoption
- » IBM Hybrid Transaction Analytic Processing HTAP
- » What is Hybrid Transaction/Analytical Processing (HTAP)?
- » CIOs and the Ongoing Data Persistence Struggle
- » Eight Big Trends in Big Data Analytics
- » Hybrid Trasansaction/Analytical Processing (HTAP)
- » Hybrid Trasansactional/Analytical Processing (HTAP)
- » HTAP and The Future of Data
- » Mega-KV, A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores
- » SDN Note
- » My Top 10 Assertions about Data Warehouses
- » RAMCloud Overview
- » RAMCloud
- » The Virginian Database
- » Massive Throughput Database Queries with LLVM on GPUs
- » Apache Tajo Internal
- » Readings in Data Management
- » Database Management Systems
- » Planning the Approach
- » Database Management Systems
- » Readings in Database Systems
- » Query Optimization Bibliography
- » PostgreSQL
- » Advantages and Drawbacks of Using Stored Procedures for Processing Data
- » What is SQL-on-Hadoop?
- » What does “100 times faster than Hive” actually mean?
- » GraphX Programming Guide
- » TPC Benchmark
- » Hadoop 2.4.0 Installation on Mac OS X Mavericks
- » Choosing distribution models
- » Hadoop Sector will Have Annual Growth of 58% for 2013-2020
- » Why Impala Continues to Lead?
- » Apache Spark Resource Management and YARN App Models
- » Measures of Query Cost
- » Query Processing
- » Query Transformation
- » Database Conferences
- » Query Optimizer
- » Research Topics in Database Systems
- » ACID Properties of Transactions
- » Introduction to Distributed Database Management Systems
- » Introduction to Data Warehousing
- » Date's Twelve Rules for a DDBMS
- » Depth of Knowledge
- » Mapping and Folding and the Map-Reduce Paradigm
- » Codd's 12 Rules
- » OLAP vs. OLTP
- » Comparison - Ad-hoc vs Stored Procedure vs Dynamic SQL
- » FINDING INFORMATION ON THE WEB
- » Advanced Metasearch Engine Technology
- » Multi-tenant Database Systems
- » Building a Hybrid Modern Data Architecture for Apache Hadoop
- » Continuous Quries Languages
- » Semantics of Relations in Continuous Queries
- » Continuous Quries as Views
- » Building the Health Information Infrastructure for the Modern ePatient
- » Continuous Query Semantics and Operators (Part II)
- » Stream Windows
- » Continuous Query Semantics and Operators (Part I)
- » Writing Input And Output Adapters
- » Stream Models
- » In-Memory Caches
- » Pattern Matching Techniques
- » Real-time Operational Intelligence
- » Real-Time Data Stream Manager (RTDSM) Interface Specification
- » Modelling in Big Data
- » Understanding trade-offs with Brewer’s CAP theorem
- » VLDB 2013
- » Impedance Mismatch
- » NoSQL History
data science
- » Latent Dirichlet Allocation Concept
- » Reinforcement Learning Course - Georgia Tech
- » Digital Transformation in a Connected World
- » What is Graph Analytics?
- » Graphs in Machine Learning
- » Social and Information Network Analysis
- » Structural Analysis and Visualization of Networks
- » Deep Learning for Database Performance Optimization
- » No Such Thing As Artificial Intelligence
- » Machine Learning as the Key to Personalized Curation
- » 22nd ACM SIGKDD Conference
- » What is Zeppelin Interpreter
- » Using Electronic Health Records for Better Care
- » 6 Big Data Analytics Use Cases for Healthcare IT
- » Four Use Cases for Healthcare Predictive Analytics, Big Data
- » Five open source Big Data projects to watch
- » Reservoir Sampling in MapReduce
- » Clinical Data Repository Versus a Data Warehouse
- » Top 50 Data Science Resources
- » Future of big data analytics
- » Five most popular similarity measures implementation in python
- » Requirements for Cluster Analysis
- » What is Cluster Analysis?
- » Cluster Analysis
- » GPU Programming for the Data Sciences
- » Statistics, Data Mining, Machine Learning and Artificial Intelligence
- » Regression Analysis
- » The Data Science Process
- » Statistical Thinking in the Age of Big Data
- » Populations and Samples
- » Thought Experiment - Meta-Definition
- » A Data Science Profile
- » The Role of the Social Scientist in Data Science
- » What is Data Science? - The Current Landscape
- » What is Data Science?
- » T-Shaped Data Scientists
- » The Variety of Data Scientists
- » Data Science, Engineering, and Data-Driven Decision Making
- » Clustering Data Scientists
design patterns
- » Dependency Injection
- » 11 Core Big Data Workload Design Patterns
- » Bigtable
- » Chubby
- » The Google File System (GFS)
- » Overview of Data Storage and Coordination Services
- » Underlying Communication Paradigms
- » Overall Architecture and Design Philosophy
- » JUnit, A Tool for Test-Driven Development
- » Concurrent Bug Patterns and How to Test Them
- » Introduction to Bug Patterns in Java
- » Foundational data architecture patterns
- » Streams Application Design
- » Filter pattern
developments
- » Visual Studio Code, CMake and LLDB
- » Extreme Team
- » Visual Studio Code - C++ Development
- » UML Tools for Python
- » Introduction to gRPC
- » Design Patterns in Python
- » Real-Time Data Processing with Apache Flink
- » Mesos Installation
- » Building Mesos
- » Traffic Prediction
- » An Introduction to Protobufs
- » Taking the I out of PaaS with Apache Mesos
- » Software Projects Built on Mesos
- » Distributed Deep Learning on Mesos
- » Apache Mesos and GPUs
- » Apache Mesos
- » Pair Programming Tips
- » In-core computation with HyperBall
- » 소프트웨어 개발의 이해를 돕기 위한 비유
- » Redmine Plugin Installation
- » Wearable Technology Trends in 2016
- » Building LuxRender on OS X
- » KIWI Installation
- » IntelliJ IDEA 15
- » Maven Support in IntelliJ IDEA
- » CUDA LLVM Compiler
- » Stanford Compilers Course
- » Unit Test using specs
- » Scala to Java
- » Privacy as a Practice in Mobile App Development
- » Function Composition
- » Scala Collection
- » Common Lisp using LLVM and C++ for Molecular Metaprogramming
- » Big Data Challenges and Opportunities
- » Rethinking SIMD Vectorization for In-Memory Databases
- » Scala idiom - Prefer immutable data structures
- » Scala - Tools and Frameworks
- » Scala - Pattern Matching
- » Scala - Functional/Collection
- » Why Scala?
- » Introduction to Scala
- » Scala - Object Oriented Features
- » Scala Installation
- » Scala Basics
- » SBT (Simple Build Tool)
- » GNUPlot Basics
- » Google Protocol Buffer Basics - Java
- » How to fix “System program problem detected” error on Ubuntu
- » Samsung Developer Day 2014 at MWC - Samsung MultiScreen SDK
- » Using Gitlab on Mac OS X
- » Gitlab Installation on Mac OS X
- » Why Functional Programming?
- » Macport Commands Summary
- » TDD Best practices
- » Test-Driven Development in a nutshell
- » Introduction to the Maven
- » Native C/C++ Like Performance For Java Object Serialization
- » Using ByteBuffer in Java
- » Esper Relational Database Adapter
- » Event Rendering to XML and JSON
- » Esper Socket Adapter
- » Esper Adapter Concept
- » Scatter plots in Python
- » Generating random numbers and plots in Python
- » JUnit, A Tool for Test-Driven Development
- » Syntax highlighting tools for github page
- » Boxplots comparing two different populations in Python
- » JAXB Class Generation From XML Schema File using JAXB Tool
- » Introduction to Esper for Java
- » My Scoop It! Sites
- » Concurrent Bug Patterns and How to Test Them
- » Introduction to Bug Patterns in Java
- » N-Screen Application Development Manual
- » Android WebView Application using Naver Open API
- » Extreme Programming
- » Git Commands
- » Git Concepts and Architecture
- » Adding a post to github page
foundation models
gpgpu
- » Gunrock
- » Compiling CUDA C/C++ with LLVM
- » gpucc; an open-source GPGPU compiler
- » Automatic Source Transformation from C++ to CUDA using Clang/LLVM
- » Map-D
- » Rethinking the Role of CPUs in Modern Computers
- » MultiQx-GPU
- » Concurrent Analytical Query Processing with GPUs
- » A GPU Parallelized Database Accelerator
- » Fundamentals of Parallel Computing with the GPU
- » Mega-KV, A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores
- » The Virginian Database
- » Massive Throughput Database Queries with LLVM on GPUs
- » Hadoop + GPU Architecture
- » MapD (Massively Parallel Database) Overview
- » Data Science using GPUs
- » IBM Juices Hadoop With Java On Tesla GPUs
- » Setup a JavaCL Maven Project
- » Some Common Parallel Patterns
- » Why does MapReduce + GPU Computing?
- » GPU Programming for the Data Sciences
- » GTC On-Demand
- » Computational Thinking
graph mining
- » What's the difference between programming and coding
- » Prompt Engineering Overview
- » Cross-Platform Data Lineage with OpenLineage
- » Continuous Training and Deployment with ZenML and Seldon
- » CVPR Highlights 2023
- » Robust and Trustworthy Deep Learning
- » PFNs; Use neural networks for 100x faster Bayesian predictions
- » Multimodal Deep Learning for Protein Engineering
- » Topological Deep Learning; Going Beyond Graph Data
- » Is Distance Matrix Enough for Geometric Deep Learning?
- » Sign and Basis Invariant Networks for Spectral Graph Representation Learning
- » Distributed and Multiagent Reinforcement Learning
- » A Distributional Multi-agent Reinforcement Learning Approach
- » Deploy and Scale Models with BentoML
- » Tool Agnostic MLOps with ZenML
- » The Four Pillars of Machine Learning
- » When to use Kubernetes natively over Kubeflow for ML
- » Deploy ZenML + Kubeflow + MLflow + Minio
- » MLOps Pipeline Tutorial with ZenML and Kubeflow
- » ML models with ZenML and BentoML
- » Recent Advances in Vision Foundation Models
- » CVPR 2023 Tutorials
- » Model Registry and Deployment with MLflow
- » How to Deploy ML Models in Production with BentoML
- » Data Science–A Systematic Treatment
- » LEARN ENGLISH with STEVE JOBS
- » DuckDB Internals
- » How to Master the Art of Leadership
- » Getting Started with ZenML
- » Generative Models for Language and Vision
- » ChatGPT, LLMs & Generative AI; What Your Business Needs to Know
- » Differential Privacy & Variants
- » Towards Learned Database Systems
- » Deep Lake; a Lakehouse for Deep Learning
- » Towards Natural Language Query Answering
- » Do We Still Need People To Write Database Systems?
- » Generative AI and Databases
- » Contrastive Learning in PyTorch
- » Self-Supervised Learning; Self-Prediction and Contrastive Learning
- » Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
- » Bayesian Neural Network
- » Study Less Study Smart
- » Kubeflow v1.7 Release Overview
- » Amazon Redshift Internals
- » ChatGPT Course – Use The OpenAI API to Code 5 Projects
- » Installing and Using NVIDIA Docker
- » Hypertree Decompositions
- » What is Platform Engineering?
- » Databricks Spark SQL / Photon
- » Google BigQuery / Dremel
- » Personalized Image Generation
- » Kubeflow Pipelines 2.0
- » Learning to Transfer Knowledge Through Embedding Spaces
- » Traffic Prediction with Transfer Learning
- » First-Passage Percolation and Related Models
- » Sparse Fourier Transform Algorithm for Real-Time Applications
- » The Frontier of Deep Learning for Robotics
- » Microsoft's Products Will Soon Access Open AI Tools Like ChatGPT
- » LLaMA & Alpaca; "ChatGPT" On Your Local Computer
- » Multi-Objective Recommender Systems
- » Hands-on Explainable Recommender Systems with Knowledge Graphs
- » Improving Recommender Systems with Human in the Loop
- » Open-Source Systems for Federated Learning
- » An Introduction to Federated Computation
- » Data Governance and Sharing on Lakehouse
- » Data Mesh and Lakehouse
- » Master Data Management
- » Setting up Big Data Fabric
- » On Future of the Modern Data Stack
- » Data Virtualization in Data Fabric
- » Recommender Systems using Graph Neural Networks
- » Activate Your Modern MetaData Stack
- » Databricks Lakehouse makes payments ingestion and analytics simple
- » The Essential Guide to Data Lineage
- » Data Governance Explained in 5 Minutes
- » Automated Data Lineage with Unity Catalog
- » Active Metadata - Understanding the magic behind the Data Fabric
- » Introduction to Data Mesh
- » PostgreSQL vs MySQL
- » Activate Your Metadata to Empower Innovation
- » Accelerating Hybrid Data Mesh Implementation
- » Deep-Dive into Delta Lake
- » Delta Lake 2.0 Overview
- » Advancing Spark - The Photon Whitepaper
- » Column-Level Lineage and Active Metadata
- » Five Things to Consider About Data Mesh and Data Governance
- » Data Mesh Implementation Patterns
- » Intro to Databricks Lakehouse Platform Architecture and Security
- » Meshing About with Databricks
- » Enterprise Data Fabric
- » Converging Advances to Accelerate Molecular Simulation
- » Learning Delaunay Surface Elements for Mesh Reconstruction
- » Data Fabric for Self-Driving Cars
- » Knowledge Graphs Seminar
- » Data Cataloging with Knowledge Graphs
- » The Foundation of a Data Fabric
- » Data Mesh, Data Fabric, Data Lakehouse
- » Tractable Probabilistic Circuits
- » Distributed Analytical Database Systems
- » GraphZeppelin - Streaming Graph Connectivity at Scale
- » Understanding Graph Data Representations in Triplestores
- » Graph-Powered Data Exploration
- » Google BigQuery
- » Conformal Prediction Tutorial
- » Traffic Prediction Paper Collection
- » End-to-end Optimization of Machine Learning Prediction Queries
- » Compute Complex Temporal Join Queries Efficiently
- » Making Learned Query Optimization Practical
- » Universal Database Optimizer
- » Machine Learning for Query Optimization
- » ML Explainability
- » Towards Generalizable Autonomy
- » Building an AI and ML ready Modern Data Platform
- » Diffusion Probabilistic Modelling of Protein Backbones in 3D
- » ICCV Authors Guidelines
- » Diffusion Probabilistic Models
- » Stable Diffusion - What, Why, How?
- » Deep Energy-Based Learning
- » Incident Prediction in Spatio-Temporal Road Graph Networks
- » Diffusion probabilistic modelling of protein backbones in 3D
- » Contrastive Learning in PyTorch
- » Neural Rendering
- » Anatomy of a Research Paper
- » Geometric Deep Learning for Drug Discovery
- » Deploy Custom Python Model Server
- » Contrastive Learning in PyTorch
- » InferenceService using a Custom Torchserve Image
- » From Machine Learning to Autonomous Intelligence
- » Towards a Learned Index Structure for ANN Search
- » Graph Processing; from Academic Research to Industrial Application
- » Katana Graph; A Graph Intelligence Platform
- » Machine Learning for Scientific Discovery
- » Graph Neural Networks as Gradient Flows
- » Ab-Initio Potential Energy Surfaces
- » Reinforcement Learning via Sequence Modeling
- » Recipe for a General, Powerful, Scalable Graph Transformer
- » Spatial Hash Grids & Tales from Game Development
- » Cardinality Estimation Benchmark
- » Spatial Indexing in PostGIS
- » Data-driven Learned Metric Index; an Unsupervised Approach
- » Synopses - Samples, Histogram, Wavelets and Sketches
- » Query Execution in MonetDB
- » Low overhead self-optimizing storage for compression
- » A Database Tuning Tool that "Reads the Manual"
- » Rethinking Graph Transformers with Spectral Attention
- » Introduction to TorchServe
- » MicroK8s Installation & Simple Commands
- » Diffusion Models from Scratch in PyTorch
- » Deep Learning Theory Lectures
- » Kubeflow Fairing
- » Kubeflow Fairing, Pipelines, and Training
- » On Recoverability of Graph Neural Network Representation
- » Deploy Model to KServe
- » TorchScript and PyTorch JIT
- » Spectra of Graphs and Hypergraphs
- » How to Serve PyTorch Models with TorchServe
- » A Novel Data Set for Information Retrieval on the Basis of Subgraph Matching
- » Graph Embedding in Vector Spaces Using Matching-Graphs
- » Google Cloud BigQuery ML Using SQL
- » Open World Lifelong Learning
- » On Machines that can Learn Continually
- » Introduction to Continual Learning
- » Introducing OpenAPI Generator
- » Kubeflow Development Environment
- » KFServing Deep Dive
- » Protein structure prediction with AlphaFold
- » Deep Learning for Scientific Computation
- » Started with AI in Drug Discovery
- » Istio Simplified
- » Knative and Microk8s with Multipass
- » KServe in Kubenetes
- » Graph Ordering Attention Networks
- » Introduction to KServe
- » Position-Aware Graph Neural Network using Reachability Estimations
- » Isomorphism Using Adjacency Matrix
- » Efficient Probabilistic Truss Indexing on Uncertain Graphs
- » Toward Verified Artificial Intelligence
- » Modern Data Stack
- » PyTorchJobClient
- » KServe (Kubeflow KFServing) Live Coding Session
- » Workflow for ML Projects — MLOps
- » Kubeflow Setup
- » Introduction to Data Mesh
- » Theory of Graph Neural Networks
- » Representational Power of Graph Nerual Networks
- » Task structure and generalization in graph neural networks
- » Geometry Processing with Neural Fields
- » Trends in AI
- » RelationalAI Knowledge Graph Management System
- » Topological Graph Neural Networks
- » Causality and (Graph) Neural Networks
- » Using Graph Neural Networks for Multi-Node Representation Learning
- » Epistemic Uncertainty Estimation for Efficient Search of Drug Candidates
- » Toward Neuro Causality
- » Connect to MySQL Database from Visual Studio Code
- » Data-Efficient Graph Grammar Learning for Molecular Generation
- » Rethinking Graph Transformers with Spectral Attention
- » How GNNs and Symmetries can help to solve PDEs
- » Neural diffusion PDEs, differential geometry, and graph neural networks
- » BigQuery ML; Machine Learning with Standard SQL
- » GNN with Learnable Structural and Positional Presentations
- » Istio Service Mesh 101
- » Graph Neural Networks and Diffusion PDEs
- » Recommender Systems using Graph Neural Networks
- » Manage Multi-tenant ML Workloads Using Istio
- » Graph Neural Networks as Neural Diffusion PDEs
- » Exploring ML Model Serving with KServe
- » Kubeflow Tutorial | Model Serving
- » Using AI to accelerate scientific discovery
- » Java to MySQL using Visual Studio Code
- » Developing the Kubernetes Python Client
- » Kubernetes Patterns
- » Using Visual Studio Code for Java Maven Project
- » Mapping External Services
- » MiniKF; Kubeflow on your laptop
- » Gateway Routing Pattern
- » Kubeflow v1.5 Release Overview
- » TensorFlow Distributed Training on Kubeflow
- » Sidecar Pattern
- » YAML
- » Kubeflow on macOS
- » Why is Kubernetes called K8s?
- » Kubernetes Crash Course for Absolute Beginners
- » Marrying Top k with Skyline Queries
- » Spatial Skyline Queries on Triangulated Irregular Networks
- » Challenges and Solutions of Using Kubernetes for Blockchain Applications
- » PyTorch Training (PyTorchJob)
- » Kubeflow Architecture
- » Istio and Service Mesh
- » Kubernetes Tutorial for Beginners
- » Serving Machine Learning Models at Scale Using KServe
- » Advanced Model Inferencing Leveraging Kubeflow Serving
- » PyTorch via SQL Commands
- » Scaling Shortest Path Graph Queries on Very Large Networks
- » Hierarchical Core Maintenance on Large Dynamic Graphs
- » Shortest Paths and Centrality in Uncertain Networks
- » Bag of Tricks for Node Classification with Graph Neural Networks
- » The Case for Learned Index Structures
- » Deep Learning for Graph Similarity Search
- » HyperSPNs; Compact and Expressive Probabilistic Circuits
- » Learned Index Structures for Dynamic and Multi-Dimensional Data
- » Blame the Data, Not the System
- » Hyperbolic Embeddings in Machine Learning and Deep Learning
- » What are Temporal Databases?
- » Git Commit Message Style Guide
- » What is PrestoDB?
- » Spatial, High Dimensional, Temporal Data Indexing and Querying
- » A Learned Spatial Index for Range and kNN Queries
- » An Introduction to Spatial Data and its Applications
- » Robustness/Interpretability in Vision & Language Models
- » Visual Data Analysis; How? When? Why?
- » SageDB; A Self-Assembling Database System
- » Towards Zero-Shot Learning for Databases
- » From Workload-Driven to Zero-Shot Learning
- » From Notebook to Kubeflow Pipelines to KFServing
- » Improving Transfer and Robustness of Supervised Contrastive Learning
- » Approximate Query Processing
- » What are Probabilistic Data Structures?
- » Approximation Algorithms for Large Scale Data Analysis
- » Modeling Relational Data with Graph Convolution Network
- » Open-source Change Data Capture With Debezium
- » Finding Approximately Repeated Patterns in Time Series
- » Looper; An End-to-End ML Platform for Product Decisions
- » Learned Indexing and Sampling for Improving Query Performance
- » Principles of Good Machine Learning Systems Design
- » Deep Learning Design Patterns
- » Tips for Thinking Like a Machine Learning Architect
- » Machine Learning Design Patterns
- » Introduction to Kubeflow
- » Martingale Theory
- » Making Architecture Matter
- » Bayesian Inference for Big Data
- » Architecture and Cardinality Estimations for Graph Queries
- » What causes congestion?
- » Optimization problems in graphs with locational uncertainty
- » Deep Implicit Layers
- » Convex Functions
- » Quantum Deep Learning
- » Uncertainty and Out-of-Distribution Robustness in Deep Learning
- » Probabilistic Methods for Increased Robustness in Machine Learning
- » Probabilistic Circuits - Representations, Inference, Learning and Theory
- » FLAT-Fast, Lightweight and Accurate Method for Cardinality Estimation
- » Learned Query Scheduling
- » A 10 Minute Introduction to Kubeflow
- » Ethics of Artificial Intelligence
- » What is AI Ethics?
- » What is the Cardinality Estimator?
- » Qd-tree - Learning Data Layouts for Big Data Analytics
- » Sum-Product Networks
- » Towards Zero-Shot Learning for Databases
- » Machine Learning Design Patterns
- » Learned DBMS Components 2.0
- » Deep Multi-task and Meta Learning
- » Machine Learning Design Patterns - Rebalancing
- » Calcite Tutorial
- » NeuroCard - One Cardinality Estimator for All Tables
- » GNN based Recommender Systems
- » On the Nature of Data Science
- » Breaking the Limit of Graph Neural Networks using Local Mixing Patterns
- » Performance Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks
- » Data Fabrics for Enterprise Data Management?
- » Query Optimization and Acceleration at Dremio
- » AI4DB & DB4AI-Papers
- » Permissioned Blockchains
- » Apache Spark, Data Science + Machine Learning
- » Introduction to Temporal Graph Neural Networks
- » Traffic Forecasting with Pytorch Geometric Temporal
- » Geometric Deep Learning
- » What Is NFT? - Non Fungible Token
- » Deep Recommender Systems
- » Weisfeiler and Lehman Go Cellular; CW Networks
- » Matrix Multiplication, and the Asymptotic Spectrum Of Tensors
- » Traffic Prediction for Intelligent Transport System
- » Revisiting Knowledge Graph Completion From a Practical Perspective
- » Applying Machine Learning-based Database Tuning in Production
- » Cloud-Native Ledger Graph Database
- » A Probabilistic Model for Joint Inference from Differential Equations
- » Optimization Frameworks for Graph Clustering
- » Neuralising a Computer Scientist
- » Preparing For Your Dissertation Defense
- » Oral Dissertation Defense Preparation
- » How To Defend Your Thesis? Top 10 Tips For Success
- » How to Get a PhD in Computer Science
- » 100 Thesis Defense Questions in 3 Categories
- » 10 Mistakes to Avoid When Defending Your Thesis
- » How to prepare for your PhD thesis defence
- » Geometric Deep Learning-Past, Present, And Future Optimization
- » Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
- » How to write a literature review - my simple 5 step process!
- » Geometric Deep Learning - from Euclid to drug design
- » Graph Isomorphism in Quasipolynomial Time
- » Geometric Deep Learning Blueprint
- » Geometric Deep Learning-The Erlangen Programme of ML
- » KDD Cup OGB Large-Scale Challenge
- » Theoretical Foundations of Data-Driven Algorithm Design
- » Relational Inductive Biases, Deep Learning, and Graph Networks
- » The Future Is Big Graphs
- » The Universal Approximation Theorem
- » Equivariant Networks and Natural Graph Networks
- » An Introduction to Hamiltonian Monte Carlo Method for Sampling
- » Recurrent Neural Networks for Cognitive Neuroscience
- » Permutation Equivariance of Graph Filters
- » Graph Convolutional and Isomorphism Networks
- » Combinatorial Properties of the Weisfeiler-Leman Algorithm
- » Advances in Self-Supervised Learning
- » Contrastive Loss, Deep Metric Learning
- » Self-Supervised Learning
- » Contrastive Learning; A General Self-supervised Learning Approach
- » Supervised Contrastive Learning
- » What is Noise-Contrastive Estimation?
- » GNNs with Learnable Structural and Positional Representation
- » Knowledge Graph Representation
- » Temporal Graph Networks (TGN)
- » Building a Recommender System using Graph Neural Networks
- » Junction Tree Variational Autoencoder for Molecular Graph Generation
- » Understanding Graph Attention Networks
- » GVAE Training and Adjacency Reconstruction
- » Graph Representation Learning for Drug Discovery
- » Graph Neural Networks for Traffic Prediction
- » Visual Exploration of Trajectory Data
- » GPS Data Analysis with Python
- » 22 Python libraries for Geospatial Data Analysis
- » Clustering Trajectories
- » Towards Causal Representation Learning
- » Random Sum Product Networks
- » Understanding & Generating Source Code with Graph Neural Networks
- » Sum-Product Networks; The Next Generation of Deep Models
- » Deep Learning to Discover Coordinates for Dynamics
- » The Power of Graph Signal Processing
- » Some Mathematical Problems in Graph Signal Processing
- » On Laplacian Eigenmaps for Dimensionality Reduction
- » Overview of Graph Embeddings
- » Sum-Product Networks; Powerful Models with Tractable Inference
- » Sum-Product Networks
- » Geometric Deep Learning and Reinforcement Learning
- » Discovering Genetic Medicines with our AI Drug Discovery Platform
- » Proof of Euler's Formula
- » GNNs and RL in Traffic Optimization in Cities
- » Graph Neural Networks and Applications to Deep Reinforcement Learning
- » Writing Scientific Papers
- » Drug Discovery with GANs
- » PyTorch nn.Embedding()
- » Reproducing Kernel Hilbert Spaces
- » Dynamical Systems and Machine Learning
- » An Introduction to Hilbert Spaces
- » Graph Neural Networks in Computational Biology
- » What is Euler's Number 'e'?
- » Euler's Formula
- » The Characteristic Equation and Eigenvalues
- » Spanning Trees and Kirchhoff's Theorem
- » Acceptance Rate of AI Conferences
- » Deep Graph Learning Foundations, Advances and Applications
- » Learning Graphs from Data; A Signal Processing Perspective
- » Making Graphs Compact by Lossless Contraction
- » A Short Course in Spectral Graph Theory
- » An Introduction to Spectral Graph Theory
- » Spectral Graph Theory; Enter Linear Algebra
- » Spectral Graph Theory; The Standard Random Walk
- » Spectral Graph Theory; The Quadratic Form
- » The Laplacian Matrices of Graphs; Algorithms and Applications
- » Pragmatic Ridge Spectral Sparsification for Large-Scale Graph Learning
- » Knowledge Graph Embeddings Tutorial; From Theory to Practice
- » How to use edge features in Graph Neural Networks
- » Graph Representation Learning
- » Papers on Graph Neural Networks
- » A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
- » Super-Constant-Pass Streaming Lower Bounds for Reachability
- » Awesone Neural ODE
- » Neural Differential Equations
- » Generative Adversarial Networks and TF-GAN
- » Fairness and Bias in Algorithmic Decision-Making
- » A Learned Sketch for Subgraph Counting
- » Learning over sets, subgraphs, and streams
- » Categorical Reparameterization with Gumbel-Softmax
- » A Deterministic Parallel APSP Algorithm and its Applications
- » How Do You Generate Synthetic Data?
- » Synthetic Tabular Data Generation
- » Decremental All-Pairs Shortest Paths in Deterministic Near-Linear Time
- » Streaming Lower Bounds for Reachability
- » Learning Deep Matrix Factorizations Via Gradient Descent
- » NVIDIA Omniverse and a Future of Shared Worlds
- » Introduction to Graph Neural Networks
- » CVPR 2021 Workshop on Autonomous Vehicles
- » Pytorch-BigGraph; A Large Scale Graph Embedding System
- » Unsupervised Intelligent Agents
- » Uncertainty, causality and generalization
- » Illustrated Guide to RNN, LSTM, and Transformers
- » Banach Space Representer Theorems for Neural Networks
- » Next-Generation Recurrent Network Models
- » Graph Representation Learning for Drug Discovery
- » PostgreSQL Optimizer Methodology
- » PPRGo-Scaling Graph Neural Networks with Approximate PageRank
- » The Energy-Based Learning Model
- » But how does bitcoin actually work?
- » Explainable AI Cheat Sheet - Five Key Categories
- » Digital Twins and Geospatial Data
- » Trends in Traffic Prediction
- » Understanding Competencies
- » Scaling Up Graph Neural Networks to Large Graphs
- » Inductive Logic Programming
- » Useful Resources for Traffic Prediction
- » A Video Game That Looks Like Reality!
- » Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
- » New lower and upper bounds for quantile summary algorithms
- » Data Summarization for Machine Learning
- » Position aware Graph Neural Networks
- » Instacart Data Description
- » Fast Counts on Big Data Systems
- » MonetDB; Scale Up Before You Scale Out
- » Kronecker Graphs, Data Generation, and Performance
- » AI+VR; The Kayfabe Life
- » Efficient Query Processing Using Machine Learning
- » How to use mathcha math online editor
- » Uncertainty estimation in supervised learning
- » Approximately Bayesian Ensembling
- » Efficient Data Processing on Modern Hardware
- » AI Bias and Fairness
- » Generative Models for Graphs
- » Evidential Deep Learning and Uncertainty
- » Learned Approximate Query Processing
- » Meta Learning Tutorial
- » Deep Learning Outline
- » Transfer Learning
- » Approximate nearest neighbor search in high dimensions
- » Learning-Based Sketching Algorithms
- » Deep Learning Under Distribution Shift
- » Principles for Tackling Distribution Shift
- » Scalable Graph Neural Networks with Deep Graph Library
- » Deep InfoMax
- » Introduction to Bandits in Recommender Systems
- » Our paper has been published in IEEE Access!
- » Constant Girth Approximation for Directed Graphs in Subquadratic Time
- » Conditionally optimal approximation algorithms
- » Sparsification of Graphs and Matrices
- » Introduction to Graph Thinking
- » Reinforcement Learning Course - Georgia Tech
- » Digital Transformation in a Connected World
- » What is Graph Analytics?
- » Graphs in Machine Learning
- » Social and Information Network Analysis
- » Structural Analysis and Visualization of Networks
- » MapGraph
- » Organizational Charts
- » Large Graph-Mining - Power Tools and a Practitioner's Guide
- » Stanford Network Analysis Platform(SNAP)
- » Community Detection - Clustering Coefficient
- » Predictive Methodologies - Analysis of Time and Space
- » Predictive Methodologies - Analysis of Space
- » Predictive Methodologies - Analysis of Social Networks
- » Theoretical Foundations of Data Mining and Predictive Analytics
- » Data Used in Predictive Policing
- » Operational Challenges of Predictive Policing
- » Forecast Vs. Prediction
- » Recommendations
- » Predictive Policing Myths and Pitfalls
- » Prediction-Led Policing Process and Prevention Methods
- » Predictive Policing
- » A Taxonomy of Predictive Methods
- » “Big Data” Promises a Revolution
- » Principal Component Analysis
- » Applications using graph representation
hadoop & mapreduce
- » Connected Components in MapReduce and Beyond
- » Michael Stonebraker's Talk
- » Running Hadoop MapReduce on Tachyon
- » What is the Future of Hadoop?
- » Hadoop + GPU Architecture
- » MapD (Massively Parallel Database) Overview
- » Data Science using GPUs
- » IBM Juices Hadoop With Java On Tesla GPUs
- » Apache Hadoop vs. IBM Platform Symphony and Infosphere BigInsights
- » How HPC is Hacking Hadoop
- » High-Performance Computing Cluster
- » Hadoop-RDMA
- » MapReduce for C - Run Native Code in Hadoop
- » 5 Ways to Make Your Hive Queries Run Faster
- » HIVE 0.14 Cost Based Optimizer (CBO) Technical Overview
- » DryadLINQ
- » Dryad
- » Join Optimization
- » Map-Side Join Vs. Join
- » What is map side join and reduce side join?
- » Hive Architecture
- » Translate from MapReduce to Apache Spark
- » An Insight into Hadoop YARN NodeManager
- » Hadoop-GIS Spatial Queries
- » Running a cluster of virtual machines with Hadoop
- » Use Parquet with Impala, Hive, Pig, and MapReduce
- » Actian Unleashes SQL Users on Hadoop with Easy Access, Faster Performance
- » Presto as a Service
- » HIVE as a Service
- » Understanding the world of SQL on Hadoop.
- » How to Develop Hadoop Tests
- » MRUnit Tutorial
- » HIVE TPC-DS Benchmark
- » Distributed Computing with Spark
- » Why Oracle Big Data SQL Potentially Solves a Big Issue with Hadoop Security
- » In-Stream Big Data Processing
- » Apache Hadoop YARN ResourceManager
- » Introduction To Pivotal's Next Generation Analytic Data Lake
- » War of the Hadoop SQL engines. And the winner is …?
- » Exploring Pivotal HD Single Node (VM)
- » How Pivotal Works
- » Parquet - Columnar Storage for the People
- » Pivotal Big Data Suite
- » Pivotal HD Introduction & Demo
- » Special "Use Cases" Big Data & Brews - Hortonworks, Pivotal, MapR
- » What is Apache Drill?
- » Drilling into Big Data with Apache Drill
- » Fun with TPC-H (Part 1)
- » Implementing Drill Math Functions
- » Presto By Facebook – A SQL Engine
- » Big Data - The Hadoop Data Warehouse
- » Building a Data Warehouse with Hadoop
- » Why does MapReduce + GPU Computing?
- » Apache Spark Documentation
- » Google Cloud Dataflow, a Cloud-native data processing service
- » Selecting the right SQL-on-Hadoop engine to access big data
- » Big Data Benchmark
- » Apache Spark
- » Optiq
- » Adding Acid Transactions in Hive
- » Yarn The Key to Overcoming the Challenges of Broad based Hadoop Adoption
- » Challenges of Implementing an Advanced SQL Engine on Hadoop
- » SparkR Enabling Interactive Data Science at Scale on Hadoop
- » Hbase Read High Availability Using Eventually Consistent Region Replicas
- » How Hadoop and Hive Change the Data Warehousing Game Forever
- » Big Data Management System Smart SQL Processing
- » Query Optimization Techniques and JIT based Vectorized Engine
- » Pig on Tez - Low Latency ETL with Big Data
- » Cost Based Query Optimization in Hive
- » Low Latency SQL on Hadoop
- » Building a Unified Data Pipeline in Apache Spark
- » Apache Storm Basics
- » M7 - Native Storage for MapR Tables
- » Hadoop Sector will Have Annual Growth of 58% for 2013-2020
- » Why Impala Continues to Lead?
- » Development Tools for Big Data
- » Apache Tajo JDBC Driver
- » Apache Tajo HCatalog Integration
- » Spark for Data Science; A Case Study
- » Apache Tajo Backup and Restore
- » Apache Tajo Table Partitioning
- » An Insight into Hadoop Yarn Resource Manager
- » Apache Tajo SequenceFile
- » Apache Tajo Parquet
- » How Apache Hadoop YARN HA Works
- » Apache Tajo RCFile
- » Apache Tajo Table Management
- » Apache Tajo CSV (TextFile)
- » Papers to Read
- » Apache Tajo Functions
- » Apache Tajo SQL Language
- » Apache Tajo Shell
- » Apache Tajo™ 0.8.0 Configuration
- » Apache Tajo™ 0.8.0 Released!
- » Apache Tajo™ 0.8.0 Documentation
- » The Stinger Initiative From HortonWorks With Hadoop
- » Bigtable
- » Chubby
- » Big Data & Brews - Ari Zilka, CTO of Hortonworks' Talk
- » The Google File System (GFS)
- » Stinger Intiative - Making Apache Hive 100 Times Faster
- » Stinger - Interactive Query for Hive
- » Overview of Data Storage and Coordination Services
- » Underlying Communication Paradigms
- » Overall Architecture and Design Philosophy
- » Comparing Apache Tez and Microsoft Dryad
- » Apache Spark and Apache Tez
- » Introducing the case study - Google
- » Integration Tajo with Hive
- » Choosing the Right SQL-on-Hadoop
- » How to Run a Simple Apache Spark App in CDH 5
- » NewSQL Vs. SQL on Hadoop
- » Presto as a Service
- » Apache Hadoop YARN – Related Work
- » Adding ACID to Apache Hive
- » Eight SQL on Hadoop Challenges
- » 11 SQL-on-Hadoop Tools
- » Healthcare Does Hadoop
- » Processing streaming data in Hadoop with Apache Storm
- » Interactive Query for Hadoop with Apache Hive on Apache Tez
- » Introducing Apache Tez 0.4
- » Big Data Genomics Sequencing
- » Failures in YARN
- » Failures in Classic MapReduce
- » YARN Framework and Fault Tolerance
- » Apache Hadoop YARN – Node Manager (NM)
- » Hardening Hadoop for Healthcare with Project Rhino
- » Apache Tajo
- » Build Instructions for Apache Tajo
- » Apache Hadoop YARN – Application Master (AM)
- » Apache Hadoop in 2013 - The State of the Platform
- » Apache Hadoop YARN – Architecture Overview
- » Apache Hadoop YARN – Resource Manager (RM)
- » Apache Hadoop YARN – History and Rationale, Shared Clusters
- » Network Topology and Hadoop
- » Modern Oil & Gas Architectures Built with Hadoop
- » Using Avro in MapReduce Jobs with Pig
- » Using Avro in MapReduce Jobs with Java
- » Using Avro in MapReduce Jobs with Hive
- » Using Avro in MapReduce Jobs with Hadoop Streaming
- » MapReduce and Hadoop Papers in the VLDB
- » MapReduce and Hadoop Algorithmic in Academic Papers
- » Hive on Tez
- » Functional Requirements of Hive on Tez Phase I
- » Query Planning of Hive on Tez
- » Design of Hive on Tez
- » Apache Tez 0.3 Released!
- » Apache Drill
- » Apache Hadoop YARN – History and Rationale
- » As MapReduce fades, Apache Spark is now a top-level project
- » Apache Hadoop YARN – ResourceManager
- » Apache Hadoop YARN – Yet Another Resource Negotiator
- » Apache Hadoop YARN – NodeManager
- » Apache Hadoop YARN – Concepts and Applications
- » Apache Hadoop YARN – Background and Overview
- » Papers about MapReduce
- » Introducing Apache Hadoop YARN
- » Tez Design - Introduction
- » Building a Hybrid Modern Data Architecture for Apache Hadoop
- » Writing a Tez Input, Processor and Output
- » Runtime API in Apache Tez
- » Re-Using Containers in Apache Tez
- » Introducing Tez Sessions
- » Data Processing API in Apache Tez
- » Apache Tez Dynamic Graph Reconfiguration
- » Apache Tez A New Chapter in Hadoop Data Processing
- » Introduction to the Tez
- » YARN (MapReduce 2)
htap
- » A Better B-Tree in Hekaton
- » The Bw-Tree; A B-tree for New Hardware
- » Hekaton Research
- » Distributed Transactions
- » ACID Isolation Level
- » Notes About Memory Allocation Redesign
- » Postgres Modifications
- » The Databaseology Lectures
- » C++ Arena Allocation Guide
- » Region-based Memory Management
- » What is VoltDB?
- » Peloton Architecture
- » Insert-Only Implementation Strategies
- » Understanding the SQL Server Columnstore Index
- » Hekaton Breaks Through
- » Compensating Transaction
- » In-Memory Column Store Architecture Overview
- » Advantages & Disadvantages of an In-Memory Database
- » Main Memory DBMS
- » HYBRID WORKLOADS AND HTAP
- » Peloton
- » MVCC Overview
- » SAP HANA
- » HYRISE
- » Main Compensation and Delta Compensation
- » Aggregate Cache
- » MonetDB Source compilation on OS X
- » What is HTAP?
- » Using HyPer with PostgreSQL Drivers
- » Hyper
- » From Big Data to Fast Data
- » SAP Hana In-Memory Explained In Nine Minutes
- » Business unIntelligence
- » Sharing Experiences in Cloud Adoption
- » IBM Hybrid Transaction Analytic Processing HTAP
- » CIOs and the Ongoing Data Persistence Struggle
- » Hybrid Trasansaction/Analytical Processing (HTAP)
- » Hybrid Trasansactional/Analytical Processing (HTAP)
- » HTAP and The Future of Data
machine learning
- » Prof. Chris Bishop's NEW Deep Learning Textbook!
- » Towards Conversational Diagnostic AI
- » Anomaly Detection in Continuous-Time Temporal Provenance Graphs
- » Gemini Full Breakdown + AlphaCode 2 Bombshell
- » Googles GEMINI Just SHOCKED The ENTIRE INDUSTRY!
- » Multimodal Prompting with a 44-minute Movie
- » Approximate Maximum Matching
- » Prompt Engineering, RAG, and Fine-tuning
- » Building Production-Ready RAG Applications
- » Entropy & Mutual Information in Machine Learning
- » Towards Federated Analytics with Local Differential Privacy
- » The Science of LLM Benchmarks - Methods, Metrics, and Meanings
- » Formal Methods and Machine Learning
- » Formal Methods and Deep Learning
- » HyperPCM-Robust Task-Conditioned Modeling of Drug-Target Interactions
- » Objective-Driven AI
- » Explainability of Graph Neural Networks
- » Why should you care about DuckDB?
- » Dynamic Graph Information Bottleneck
- » Diffusion Models on Sampling Rare Events
- » Deep Probabilistic Models with Tractable Inference
- » Nuggets of Shannon Information Theory
- » Large Language Models as Copilots for Theorem Proving
- » Application Development using Large Language Models
- » A New 3D Digital Human Creator For All!
- » 나의 문화유산답사기 30주년
- » The Fifth Paradigm of Scientific Discovery
- » Bringing analytical SQL directly to your Python shell
- » Decoding the Information Bottleneck in Self Supervised Learning
- » Information Bottleneck Theory of Deep Learning
- » MultiDiffusion; Fusing Diffusion Paths for Controlled Image Generation
- » In-Process Analytical Data Management with DuckDB
- » Generalization in diffusion models
- » Gemini Multimodal RAG Applications with LangChain
- » Exciting Trends in Machine Learning
- » How to Build a Metadata Driven Data Pipelines with Delta Live Tables
- » Learn How to Build Real-Time Warehouses on Lakehouse
- » Databricks Lakehouse Overview Training
- » Simplify ETL pipelines on the Databricks Lakehouse
- » Delta Live Tables A to Z; Best Practices for Modern Data
- » Building Reliable ETL Pipelines with Azure Databricks
- » Graph-based Virtual Sensing
- » Online A/B Testing of Real-Time Event Detection Systems
- » Machine Unlearning of Features and Labels
- » Machine Unlearning Papers
- » Ecommerce Marketing Analytics; Top 8 Metrics EXPLAINED
- » Differential Privacy and Machine Unlearning
- » Foundation Models on Consumer Devices
- » Lakehouse with Delta Lake Deep Dive Training
- » Large Language Models for Program Optimization
- » Multimodal Reasoning; PaLM-E & Gemini
- » Text2SQL; The Dream versus Reality
- » Data Lakehouse and Data Mesh—Two Sides of the Same Coin
- » Pathways Language Model and Model Scaling
- » The Information Bottleneck Approaches in Deep Neural Networks
- » On Mutual Information Estimation
- » A Deeper Understanding of Deep Learning
- » The Information Bottleneck Theory of Deep Learning
- » Information Theory of Deep Learning
- » DeepMind Gemini 1.5 - An AI That Remembers!
- » Graph Machine Learning for 2024
- » Unlock Data Insights Faster with AI-Generated Sample Data
- » Graph Dashboards With LLM Powered Natural Language Queries
- » Approximate Nearest Neighbor and Product Quantizer
- » Choosing the Right Embedding Model, Chunking Strategy, and More
- » Document Curation for Retrieval Augmented Generation
- » Data Federation with Unity Catalog
- » The Data Intelligence Platform
- » Cross-Platform Data Lineage with OpenLineage
- » Mitigating LLM Hallucinations
- » Towards Foundation Models for Knowledge Graph Reasoning
- » Unveiling the Power of Multimodal RAG for Images and Text
- » Image Similarity Search with Vector Databases
- » Building a Music Recommender with Vector Databases
- » Document Semantic Search with Vector Databases
- » Sentiment Analysis with Vector Databases
- » Overview of RAG Approaches with Vector Databases
- » Graph-Based Approximate Nearest Neighbors (ANN) and HNSW
- » Enhancing Vector Database Queries with Metadata Filtering
- » Guiding Graph Embeddings using Path-Ranking Methods
- » Evaluating RAG Performance with Vector Databases
- » Chunking Best Practices for RAG Applications
- » Product quantization in Faiss and from scratch
- » Ambient Computing, Multimodal AI & Edge Computing
- » Composite AI, the Future of Computing and Human Capital
- » From Delta Sharing to Clean Rooms
- » What is a vector database?
- » Advanced RAG; Chunking, Embeddings, and Vector Databases
- » Unity Catalog Overview
- » How to Build LLMs on Your Company’s Data While on a Budget
- » A Technical Deep Dive into Unity Catalog's Practitioner Playbook
- » Which is the most reliable estimate?
- » Building an LLMOps Stack for Large Language Models
- » Introduction to large language models
- » Cost Optimization use cases with a Data Catalog
- » 5 Explosive Digital Marketing Strategies
- » Introduction to Vector Search
- » Atlan AI for Documentation -- LIVE Demo
- » LLMOps; Everything You Need to Know to Manage LLMs
- » How to Train Your Own Large Language Models
- » The future of generative AI
- » Reset KOREA!, The 1st Seminar
- » Intro to Large Language Models
- » Metadata Use Cases Demo in Atlan
- » The Anatomy of an Active Metadata Platform
- » 3 Keys for Collective Intelligence
- » Towards Self-Service Analytics
- » Self-designing Data Systems for the AI Era
- » Active Metadata Management
- » Active Metadata with Data Quality
- » The Next Generation of Active Metadata Platforms
- » Knowledge Graph Reasoning with Graph Neural Networks
- » The Modern Metadata Platform
- » Metadata Management & Data Catalog
- » Temporal Graph Benchmark for Machine Learning on Temporal Graphs
- » Safely Using Gen AI With Active Metadata and Data Fabric
- » A General Graph Neural Network Framework for Link Prediction
- » Neural Bellman-Ford Networks
- » Towards Interpretable Data Science
- » Differential Privacy And The Complexity Of Simple Queries
- » Privacy Preserving AI
- » The Promise of Differential Privacy
- » A Survey on Graph Neural Networks for Time Series
- » Towards Foundation Models for Knowledge Graph Reasoning
- » Stylish Hierarchical Navigable Small World Indexes
- » Retrieval-Augmented Language Model Pre-Training
- » Domain Knowledge Augmented AI for Databases
- » AND/OR Search Spaces for Anytime Probabilistic Reasoning
- » Generative AI And Programming
- » Gartner's Top 10 Tech Trends for 2024
- » Making Retrieval Augmented Generation Better
- » Emerging architectures for LLM applications
- » Tree of Thought Prompting
- » Building a managed database service with Kubernetes Operators
- » A Purpose-Built Vector Data Management System
- » Best Practices for Query Performance Testing
- » OpenAI Embeddings and Vector Databases Crash Course
- » Long-Range Genomic Sequence Modeling
- » How to Choose a Vector Database
- » Awesome Vector Database
- » Vector Databases simply explained! (Embeddings & Indexes)
- » OpenAI DevDay, Opening Keynote
- » Knowledge Distillation
- » Vector Database for Large Language Models in Production
- » Embeddings, Vector Databases, and Search
- » Fast, Approximate Vector Queries on Very Large Unstructured Datasets
- » How I make beautiful GRAPHS and PLOTS using LaTeX
- » Transformers for Multimodal Self Supervised Learning
- » Lance; A Modern Columnar Data Format
- » Feature Store Architectures & Technical Challenges
- » Machine Learning for Multi-Scale Molecular Simulation and Design
- » Reasoning Algorithmically; from Toy Experiments to AGI Modules
- » Table Representation Learning
- » Language Models and Applications in Data Management
- » Categories for AI 1; Why Category Theory?
- » Estimating Separable Matching Models
- » Action Matching; Learning Stochastic Dynamics from Samples
- » Categories, Graphs, Reasoning
- » Heterogeneous Continual Learning
- » Tractable Control for Autoregressive Language Generation
- » Approximating the TV Distance Between Two Product Distributions
- » The Rise of Tractable Circuits
- » Tractable Bounding of Counterfactual Queries
- » A Tutorial on Causal Representation Learning
- » Explainable Data Drift for NLP
- » Open-Source Spotlight - Deepchecks
- » Develop test suites for machine learning models and data
- » Kolmogorov-Smirnov Test Explained
- » L-Infinity Distance Explained
- » Wasserstein Distance Explained
- » Jensen-Shannon Distance Explained
- » Comparison of Data Drift Detection Methods
- » Sketching and Projecting
- » Randomized Embeddings and Neural Networks
- » Memory-Regret Tradeoff for Online Learning
- » Sketching Techniques for Kernel Density Estimation
- » Fast Attention Requires Bounded Entries
- » From Machine Learning to Autonomous Intelligence
- » Less is More with PostgresML
- » The Power of Graph Learning
- » AI-Powered Database Optimization as a Service
- » Open Source Vector Search Engine and Vector Database
- » How to evaluate and explore data drift in ML systems
- » Hugging Face Tutorial
- » Python and PyTorch Tutorial
- » Data Drift & Model Drift Detection Using Python
- » Easiest way to Detect Data Drift Using Evidently
- » Data Drift Monitoring in Azure ML
- » Drifting Away; Testing ML Models in Production
- » Multimodal Deep Learning
- » Automating Model Monitoring and Drift Detection
- » Monitoring Model Quality in Streaming Machine Learning Applications
- » ML Drift; Identifying Issues Before You Have a Problem
- » Dynamic Graph Algorithms; What We Know and What We Don’t
- » How to Understand Clearly
- » Gentle Introduction to Causal Inference
- » Awesome Incremental Learning / Lifelong learning
- » Temporal Distributional Shifts
- » A Brief Introduction to Causal Inference
- » Explainable Deep Learning for Structured Data
- » Leveraging Privacy-Enhancing Technologies with LLMs
- » Probabilistic and Logical Circuits for Tractable Causal Reasoning
- » Probabilistic Reasoning and Learning for Trustworthy AI
- » Learned Query Optimiser At the Forefront of AI Driven Databases
- » Internals of Permissioned Blockchains
- » On the Role of Neural Collapse in Few-Shot Transfer Learning
- » Introduction to Data Structures and Optimization for Fast Algorithms
- » Attention Approximates Sparse Distributed Memory
- » Fine-Grained Complexity for Dynamic Graph Problems
- » Sketching
- » Graph Data Structures
- » Graph Linear Algebra
- » Kubeflow vs MLFlow
- » Language Models as Statisticians, and as Adapted Organisms
- » Differentially Private Sampling from Distributions
- » HNSW for Vector Search Explained and Implemented with Faiss
- » Incremental View Maintenance
- » Preserving Data Privacy in Federated Learning
- » On Localization in Language Models
- » Meaning in the age of large language models
- » Large Language Models Meet Copyright Law
- » Logic and Probabilistic Circuits
- » Prompt Engineering
- » Statistical Learning Theory and Neural Networks
- » LangChain "Advanced Retrieval" Webinar
- » Challenges and Opportunities in Differentially Private Federated Learning
- » LlamaIndex Overview & Use cases
- » In-Context Learning
- » 9 tips to help you PROVE MATH THEOREMS
- » Are LLMs the Beginning or End of NLP?
- » Bayesian Principles for Machine Learning
- » How to Speak
- » Differentially Private GNNs with Aggregation Perturbation
- » Recent Advances in Causality-based Recommendation
- » Interpretability via Symbolic Distillation
- » Synthesizing Relational Data with Differential Privacy
- » PrivSyn - Differentially Private Data Synthesis
- » Basics of Neurosymbolic Architectures
- » The Theory and Practice of Differential Privacy
- » Towards Reliable Use of Large Language Models
- » Cartel Screening and Machine Learning
- » Local Differential Privacy in Practice
- » Survey on Privacy/Security in FL
- » NVIDIA Keynote at SIGGRAPH 2023
- » Trustworthy AI - A Computational Perspective
- » High-Dimensional Bayesian Optimization
- » Self-Supervised Learning in Recommendation
- » Keeping AI under control through mechanistic interpretability
- » Towards Machines that can Learn, Reason, and Plan
- » Diffusion and Score-Based Generative Models
- » Differentially Private Diffusion Models
- » Foundation Models and Fair Use
- » Spatial Data Science Methods for Improving Models
- » Predictive Whittle Networks for Time Series
- » There's no such thing as MIRACLE
- » Locally Private Graph Neural Network
- » What is a Vector Database?
- » How to write Tree of Thoughts Prompts
- » What's the difference between programming and coding
- » Prompt Engineering Overview
- » A New Upper Bound for the Heilbronn Triangle Problem
- » AI and the Future of Humanity
- » Two Paths to Intelligence
- » Large Multimodal Models
- » ChatGPT for Data Analysts
- » Visual Prompting Livestream With Andrew Ng
- » Quantifying and Understanding Memorization in Deep Neural Networks
- » Cross-Platform Data Lineage with OpenLineage
- » Continuous Training and Deployment with ZenML and Seldon
- » CVPR Highlights 2023
- » Robust and Trustworthy Deep Learning
- » PFNs; Use neural networks for 100x faster Bayesian predictions
- » Multimodal Deep Learning for Protein Engineering
- » Topological Deep Learning; Going Beyond Graph Data
- » Is Distance Matrix Enough for Geometric Deep Learning?
- » Sign and Basis Invariant Networks for Spectral Graph Representation Learning
- » Distributed and Multiagent Reinforcement Learning
- » A Distributional Multi-agent Reinforcement Learning Approach
- » Deploy and Scale Models with BentoML
- » Tool Agnostic MLOps with ZenML
- » The Four Pillars of Machine Learning
- » When to use Kubernetes natively over Kubeflow for ML
- » Deploy ZenML + Kubeflow + MLflow + Minio
- » MLOps Pipeline Tutorial with ZenML and Kubeflow
- » ML models with ZenML and BentoML
- » Recent Advances in Vision Foundation Models
- » CVPR 2023 Tutorials
- » Model Registry and Deployment with MLflow
- » How to Deploy ML Models in Production with BentoML
- » Data Science–A Systematic Treatment
- » LEARN ENGLISH with STEVE JOBS
- » DuckDB Internals
- » How to Master the Art of Leadership
- » Getting Started with ZenML
- » Generative Models for Language and Vision
- » ChatGPT, LLMs & Generative AI; What Your Business Needs to Know
- » Differential Privacy & Variants
- » Towards Learned Database Systems
- » Deep Lake; a Lakehouse for Deep Learning
- » Towards Natural Language Query Answering
- » Do We Still Need People To Write Database Systems?
- » Generative AI and Databases
- » Contrastive Learning in PyTorch
- » Self-Supervised Learning; Self-Prediction and Contrastive Learning
- » Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
- » Bayesian Neural Network
- » Study Less Study Smart
- » Kubeflow v1.7 Release Overview
- » Amazon Redshift Internals
- » ChatGPT Course – Use The OpenAI API to Code 5 Projects
- » Installing and Using NVIDIA Docker
- » Hypertree Decompositions
- » What is Platform Engineering?
- » Databricks Spark SQL / Photon
- » Google BigQuery / Dremel
- » Personalized Image Generation
- » Kubeflow Pipelines 2.0
- » Learning to Transfer Knowledge Through Embedding Spaces
- » Traffic Prediction with Transfer Learning
- » First-Passage Percolation and Related Models
- » Sparse Fourier Transform Algorithm for Real-Time Applications
- » The Frontier of Deep Learning for Robotics
- » Microsoft's Products Will Soon Access Open AI Tools Like ChatGPT
- » LLaMA & Alpaca; "ChatGPT" On Your Local Computer
- » Multi-Objective Recommender Systems
- » Hands-on Explainable Recommender Systems with Knowledge Graphs
- » Improving Recommender Systems with Human in the Loop
- » Open-Source Systems for Federated Learning
- » An Introduction to Federated Computation
- » Data Governance and Sharing on Lakehouse
- » Data Mesh and Lakehouse
- » Master Data Management
- » Setting up Big Data Fabric
- » On Future of the Modern Data Stack
- » Data Virtualization in Data Fabric
- » Recommender Systems using Graph Neural Networks
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- » Architecture and Cardinality Estimations for Graph Queries
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- » FLAT-Fast, Lightweight and Accurate Method for Cardinality Estimation
- » Learned Query Scheduling
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- » Machine Learning Design Patterns
- » Learned DBMS Components 2.0
- » Deep Multi-task and Meta Learning
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- » Calcite Tutorial
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- » On the Nature of Data Science
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- » Query Optimization and Acceleration at Dremio
- » AI4DB & DB4AI-Papers
- » Permissioned Blockchains
- » Apache Spark, Data Science + Machine Learning
- » Introduction to Temporal Graph Neural Networks
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- » What Is NFT? - Non Fungible Token
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- » Weisfeiler and Lehman Go Cellular; CW Networks
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- » Revisiting Knowledge Graph Completion From a Practical Perspective
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- » Preparing For Your Dissertation Defense
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- » Advances in Self-Supervised Learning
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- » Self-Supervised Learning
- » Contrastive Learning; A General Self-supervised Learning Approach
- » Supervised Contrastive Learning
- » What is Noise-Contrastive Estimation?
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- » GVAE Training and Adjacency Reconstruction
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- » The Power of Graph Signal Processing
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- » On Laplacian Eigenmaps for Dimensionality Reduction
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- » Sum-Product Networks; Powerful Models with Tractable Inference
- » Sum-Product Networks
- » Geometric Deep Learning and Reinforcement Learning
- » Discovering Genetic Medicines with our AI Drug Discovery Platform
- » Proof of Euler's Formula
- » GNNs and RL in Traffic Optimization in Cities
- » Graph Neural Networks and Applications to Deep Reinforcement Learning
- » Writing Scientific Papers
- » Drug Discovery with GANs
- » PyTorch nn.Embedding()
- » Reproducing Kernel Hilbert Spaces
- » Dynamical Systems and Machine Learning
- » An Introduction to Hilbert Spaces
- » Graph Neural Networks in Computational Biology
- » What is Euler's Number 'e'?
- » Euler's Formula
- » The Characteristic Equation and Eigenvalues
- » Spanning Trees and Kirchhoff's Theorem
- » Acceptance Rate of AI Conferences
- » Deep Graph Learning Foundations, Advances and Applications
- » Learning Graphs from Data; A Signal Processing Perspective
- » Making Graphs Compact by Lossless Contraction
- » A Short Course in Spectral Graph Theory
- » An Introduction to Spectral Graph Theory
- » Spectral Graph Theory; Enter Linear Algebra
- » Spectral Graph Theory; The Standard Random Walk
- » Spectral Graph Theory; The Quadratic Form
- » The Laplacian Matrices of Graphs; Algorithms and Applications
- » Pragmatic Ridge Spectral Sparsification for Large-Scale Graph Learning
- » Knowledge Graph Embeddings Tutorial; From Theory to Practice
- » How to use edge features in Graph Neural Networks
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- » A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
- » Super-Constant-Pass Streaming Lower Bounds for Reachability
- » Awesone Neural ODE
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- » A Learned Sketch for Subgraph Counting
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- » A Deterministic Parallel APSP Algorithm and its Applications
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- » Vision Transformer - Keras Code Examples
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- » Introduction to Pytorch Geometric
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- » Graph Neural Networks - Models and Applications
- » Matching of Matching Graphs
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- » Inductive Representation Learning on Large Graphs
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- » Limitations of Graph Neural Networks
- » Choice of Graph Representation
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- » Applications of Graph ML
- » Machine Learning with Graphs
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- » Graph Neural Networks and Knowledge Graph Completion
- » Parallel and Distributed Computing in Python with Dask
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- » Mathematical Foundations of the GraphBLAS and Big Data
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- » Papers on Recommendation Systems
- » Representational Power of Graph Neural Networks
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- » Graph Neural Networks
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- » A Blueprint of Standardized and Composable Machine Learning
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- » Towards Lifelong Learning Machines (L2L)
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- » Adversarial Examples and Human-ML Alignment
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- » Explainable AI - Methods, Applications & Recent Developments
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- » Transfer Learning - Part II
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- » Graph Embeddings
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- » Learning to Learn
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- » A History of Spectral Graph Theory and its Applications
- » The JPEG Compression Algorithm
- » Transfer Learning
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- » Ian Goodfellow-Generative Adversarial Networks (GANs)
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- » Variational Inference-Foundations and Modern Methods
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- » Deep Compression
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- » Deep Learning for Recommender Systems
- » Awesome Knowledge Distillation
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- » BMM Summer Course 2018 Slides
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- » Project Malmo – a platform for fundamental AI research
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- » Variational Inference - Foundations and Innovations
- » The GAN Zoo
- » Speed at Scale - Using GPUs to Accelerate Analytics for Extreme Use Cases
- » SpatialHadoop - A MapReduce Framework for Big Spatial Data
- » From Deep Learning of Disentangled Representations to Higher-level Cognition
- » NVIDIA - AI for Generating High-Resolution Images
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- » Applied Time Series Econometrics in Python and R
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- » Processing 100 Billion IoT Events
- » Predicting congestion on London's roads with TensorFlow
- » Improving Traffic Prediction Using Weather Data
- » Pruning Makes Faster and Smaller Neural Networks
- » NIPS 2017 Proceedings
- » Distributed TensorFlow
- » This Autonomous Robot Models Your House Interior
- » Reproducibility in Deep Reinforcement Learning
- » Google's Text Reader AI
- » SLAC Dataset From MIT and Facebook
- » DeepMind Control Suite
- » Data Mining Lecture Slides
- » AI Research Trends
- » Data-Driven Mesoscopic Simulation Models
- » Introduction to Geometric Deep Learning
- » Geometric Deep Learning Paper and Code
- » Geometric Deep Learning
- » Building Machines That Learn and Think Like People
- » TensorTraffic
- » Universal Neural Style Transfer
- » Testing Software in the Age of Machine Learning
- » Effective TensorFlow for Non-Experts
- » Gartner Top 10 Strategic Technology Trends 2018
- » Transferring AI To The Real World (OpenAI)
- » Natural Language Processing with Deep Learning
- » Practical Machine Learning Tutorial with Python
- » DeepMind's AI Learns Superhuman Relational Reasoning
- » Multi-Agent Reinforcement Learning
- » Knowledge Based Artificial Intelligence Part 3 ~ 5
- » Knowledge Based Artificial Intelligence Part 2
- » Knowledge Based Artificial Intelligence Part 1
- » Advanced Analytics with Spark
- » AI Learns to Synthesize Pictures of Animals
- » Latent Dirichlet Allocation Concept
- » Mining Massive Data Sets
- » Deep Photo Style Transfer
- » Reinforcement Learning Course - Georgia Tech
- » Digital Transformation in a Connected World
- » What is Graph Analytics?
- » Gunrock - GPU Graph Analytics
- » Graphs in Machine Learning
- » AI Creates 3D Models From Faces
- » Social and Information Network Analysis
- » Structural Analysis and Visualization of Networks
- » Learning Local Geometric Descriptors from RGB-D Reconstructions
- » Machine Learning APIs by Example
- » Distributed Optimization via Alternating Direction Method of Multipliers
- » Learning and Adaptation
- » Semantic Scene Completion from a Single Depth Image
- » Latent Space Model for Road Networks to Predict Time-Varying Traffic
- » Reinforcement Learning
- » Deep Reinforcement Learning
- » Analysis and Prediction of Spatiotemporal Traffic Congestion
- » Reinforcement Learning Papers
- » Technological Singularity
- » Three Applications of Deep Learning in Big Data Analytics
- » Optimization for Machine Learning
- » Unsupervised Representation Learning
- » Generalization and Equilibrium in Generative Adversarial Networks
- » How to train a GAN
- » Generative Adversarial Networks (GANs)
- » Deep Advances in Generative Modeling
- » A Guide to Edge IoT Analytics
- » Edge Accelerated Web Platform
- » Edge Computing - IoT and Thinking Machine
- » Top AI and Machine Learning Conferences in 2017
- » Fast Data at Internet Scale with Amazon ElastiCache for Redis
- » Fast Data, Fast Monitoring
- » Geometric Detail Transfer
- » Generative Adversarial Networks - Part III
- » Generative Adversarial Networks - Part II
- » Image Editing with Generative Adversarial Networks
- » Generative Adversarial Networks - Part I
- » Can we build AI without losing control over it?
- » The Review Process for NIPS 2016
- » Guidelines for Writing a Good NIPS Paper
- » Graph Streams and Sketches
- » CNNs in Practice
- » Proceedings of The ICML 2016
- » Acceptance Ratio Statistics for Computational Intelligence Conferences
- » Machine Learning Papers in AAAI Conference 2017
- » Three things you need to know about machine learning
- » Artificial Intelligence 3.0 – Third Wave of AI
- » Wide & Deep Learning; Memorization + Generalization with TensorFlow
- » Keynote - TensorFlow Dev Summit 2017
- » Recent Results in Cloud Robotics
- » Theory, Practice, and Standardization of Eye-tracking Technology
- » Understanding 3D Shapes With AI
- » Occam's Razor
- » Massive Exploration of Neural Machine Translation Architectures
- » 5 Technology Trends in 2017 that Change Your Way of Living
- » The Evolution of Artificial Intelligence
- » How Deep Neural Networks Work
- » Stable Neural Style Transfer
- » GPU accelerated population annealing algorithm
- » Learning Sound Representations from Unlabeled Video
- » Modeling Human Communication Dynamics
- » Improving the Neural GPU Architecture for Algorithm Learning
- » How to Build an Intelligent Machine
- » Introduction to Deep Learning
- » Ten Data Acquisition Strategies for Startups
- » Automatic Creation of Sketch Tutorials
- » Deep Voice; Real-time Neural Text-to-Speech
- » Data, not algorithms, is key to machine learning success
- » The current state of machine intelligence 3.0
- » Optimizing Deep CNN-Based Queries over Video Streams at Scale
- » Gibbs Sampling-Probabilistic Graphical Models
- » AI; The New Electricity
- » Large-Scale Stochastic Learning using GPUs
- » Artificial Intelligence
- » Learning to Fill Holes in Images
- » Journals and Conferences related to Artificial Intelligence
- » AI Builds 3D Models From Images With a Twist
- » Major Conferences and Journals about Machine Learning
- » Most Cited Deep Learning Papers
- » Data Preparation for Machine Learning
- » How to Prepare Data For Machine Learning
- » Deep Natural Language Processing
- » What is Artificial Intelligence?
- » Universe
- » Game AI Development with OpenAI Universe
- » Deep Reinforcement Learning - Pong from Pixels
- » How Intelligent Machines Learn to Make Sense of the World
- » AI Makes 3D Models From Photos
- » Attention Transfer
- » Text Style Transfer
- » IBM Watson-The Power of Artificial Intelligence
- » Adaptive Machine Learning for Real-Time Streaming
- » AI and Deep Learning at Amazon
- » Datasets for Deep Learning
- » DataSets and Machine Learning
- » Open Data for Deep Learning
- » Deep Learning Program Hallucinates Videos
- » Reinforcement Learning
- » Deep Reinforcement Learning
- » Introduction to Deep Learning and Self-Driving Cars
- » Deep Learning Resources
- » Transfer Learning and Fine-tuning Deep Convolutional Neural Networks
- » Machine Learning Algorithms
- » Gunrock
- » Neural Network Learns The Physics of Fluids and Smoke
- » Recurrent Neural Networks, Image Captioning, LSTM
- » The Unreasonable Effectiveness of Recurrent Neural Networks
- » Understanding LSTM Networks
- » Mathematical Methods for Robotics, Vision, and Graphics
- » Human Pose Estimation with Deep Learning
- » WaveNet by Google DeepMind
- » IBM Watson Health
- » Large Scale Machine Learning
- » Fundamentals of Deep Learning
- » TensorFlow Tutorial
- » TensorFlow Examples
- » Deep Learning Reading List
- » Convolutional Neural Networks for Visual Recognition
- » How to read the genome and build a human being
- » Artistic Style Transfer For Videos
- » Deep Reinforcement Terrain Learning
- » The Intel® Deep Learning Framework
- » Self-Taught Learning and Unsupervised Feature Learning
- » Deep Learning - Intelligence from Big Data
- » How DeepMind's AlphaGo Defeated Lee Sedol
- » The Next Step in GPU-Accelerated Deep Learning
- » Convolutional Neural Networks
- » Awesome Deep Learning
- » Training Deep Neural Networks With Dropout
- » Advanced Operating Systems
- » Machine Learning Courses
- » Deep Learning Terms
- » 9 Cool Deep Learning Applications
- » Gartner Top 10 Tech Trends for 2016
- » CES 2016 Keynote
- » Big Data and Machine Learning - Keys to AI Progress
- » Deep Learning
- » Smart Things vs. Many Experienced Things
- » PredictionIO
nosql
- » Why NoSQL?
- » Google Spanner
- » Column-Family Stores
- » Key-Value and Document Data Models
- » Development Tools for Big Data
- » Stinger Intiative - Making Apache Hive 100 Times Faster
- » Stinger - Interactive Query for Hive
- » Introducing the case study - Google
- » Integration Tajo with Hive
- » Choosing the Right SQL-on-Hadoop
- » NewSQL Vs. SQL on Hadoop
- » Functional Programming
- » Implementing Graph-Based Applications
- » Designing Graph-Based Applications
- » NoSQL Databases
- » Key Points from NoSQL Distilled
- » The Emergence of NoSQL
- » Understanding trade-offs with Brewer’s CAP theorem
- » Impedance Mismatch
- » The Value of Relational Databases
- » What is NoSQL?
- » Neo4j
- » Graph Databases
- » NoSQL History
papers
- » How To Read a Scientific Research Paper
- » Research Topics in Database Management
- » IEEE NetSoft 2016 Call for Demonstrations
- » Papers in Parallel and Distributed Data Management
- » Telling a Story
- » The Scope of a Paper
- » Main Memory and Streaming Databases
- » Calendar of Computer Graphics Events
- » LaTeX Fonts on Mac OS X
- » Proceedings of the VLDB Endowment, Volume 9
- » Research Posters 101
- » 22nd ACM SIGKDD Conference
- » Writing-up Qualitative Research
- » How To Read A Paper
- » Paper Writing Strategy
- » Paper Writing
- » ACM TODS Manuscript Preparation
- » Distributed Computing Conferences
- » IEEE Transactions on Knowledge and Data Engineering
- » Summarizing
- » How to Write a Good Paper
- » How to Read a Technical Paper
- » SIGMOD 2015 Papers
- » How to Write a Paper Efficiently
- » Research skills
- » MapReduce and Hadoop Papers in the VLDB
- » MapReduce and Hadoop Algorithmic in Academic Papers
- » Conduct Research Survey
- » Stream Data Processing for Performance Evaluation of the Equipment in an Offshore Plant
- » Papers about MapReduce
- » Synchronization among Heterogeneous Distributed Objects
- » How to write a good research paper
- » How to overcome common obstacles to publishing in English
- » Characteristics of the Smart Devices
- » Logical Communication-based Middleware for Collaboration among Smart Apps in P2P Network Environments
- » Paper Organization
parallel computing
- » A Modern Toolkit for Microservice Communication
- » Real-Time Data Processing with Apache Flink
- » Mesos Installation
- » Building Mesos
- » An Introduction to Protobufs
- » Taking the I out of PaaS with Apache Mesos
- » Software Projects Built on Mesos
- » Distributed Deep Learning on Mesos
- » Apache Mesos and GPUs
- » What Will We Do With Quantum Computing?
- » Road Transport Systems
- » OSPRay
- » Streaming Through Caches
- » GPU Architectures and New Programming Model Features
- » Computer Architecture Essentials
- » What is Quantum Computing?
- » Neuromorphic Chip
- » Graph Partitioning
- » GPU accelerated population annealing algorithm
- » Billion-scale similarity search with GPUs
- » AI; The New Electricity
- » Large-Scale Stochastic Learning using GPUs
- » Spark-GPU; An Accelerated In-Memory Data Processing Engine on Clusters
- » How to read the genome and build a human being
- » The Art of Neural Networks
- » Multithreaded Python with OpenMP
- » Creating an SPMD Vectorizer for OpenCL with LLVM
- » Thread Local Storage
- » Readings in Parallel Computer Architecture
- » Lecture 0 - Course Description and Logistics
- » Parallel Computer Architecture Course
- » Map-D
- » Clang MapReduce
- » Rethinking the Role of CPUs in Modern Computers
- » Multiprocessor Synchronization
- » Unum Computing
- » Extreme Scale Computing
- » Computer Architecture Recommended Readings
- » Introduction to Computer Architecture
- » Lambda Expression in C++
- » Legion Bootcamp
- » RAMCloud Training
- » Kudu - New Apache Hadoop Storage for Fast Analytics on Fast Data
- » GridGain In-Memory Data Fabric
- » Advantages of Functional Programming
- » OpenMOLE
- » Advanced Vector Extension
- » Mega-KV, A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores
- » Apache Hadoop vs. IBM Platform Symphony and Infosphere BigInsights
- » How HPC is Hacking Hadoop
- » High-Performance Computing Cluster
- » Rethinking SIMD Vectorization for In-Memory Databases
- » Introduction to Docker
- » Scala idiom - Prefer immutable data structures
- » Scala - Tools and Frameworks
- » Scala - Pattern Matching
- » Scala - Functional/Collection
- » Why Scala?
- » Introduction to Scala
- » Scala - Object Oriented Features
- » Scala Installation
- » Scala Basics
- » Totally Lazy
- » The Bulk Synchronous Parallel Model
- » Data-based parallelism
- » Task-based parallelism
- » Concurrency
- » Serial/Parallel Problems
- » Multinode Computing
- » Grid Architecture and Service Modeling
- » Features of Cloud and Grid Platforms
- » Zero-Copy
- » Remote Direct Memory Access
- » GPU Programming for the Data Sciences
- » GTC On-Demand
- » Intel® Xeon Phi™ Coprocessor - the Architecture
- » GPU Cluster
- » GPU Accelerated Computing with C and C++
- » Resource Management Fundamentals
- » Advanced and Distributed Operating Systems
- » Grid Computing
- » Design Objectives of Computer Clusters
- » Clustering for Massive Parallelism
- » Why Functional Programming?
- » Parallel Computing
- » Data-intensive Computing
- » Mapping and Folding and the Map-Reduce Paradigm
- » Functional versus Procedural Programming Models
- » Parallel R
- » Computational Thinking
- » Introduction to Transactional Memory
- » Concurrent Bug Patterns and How to Test Them
presentation
- » Korea Summit 2018
- » Why we learn?
- » How To Read a Scientific Research Paper
- » How to Write a Research Plan
- » Just Do It
- » 중세 과학
- » 고대 과학
- » 학교와 직장의 차이점
- » Tell Me About Yourself - A Good Answer to This Interview Question
- » PhD Interview Questions
- » How to Improve your Clarity of Thought
- » How to Prepare and Deliver a Great TED talk
- » IELTS & TOEFL Writing - 5 Common Mistakes
- » 일상으로의 초대
- » 아주 짧은 인생을 가치 있게 보내는 법
- » 그댄 행복에 살텐데
- » The 5 Leadership Styles
- » 등록금 0원
- » Emotional Intelligence
- » Studying in Germany
- » 민물장어의 꿈
- » Sparks of Genius
- » If today were the last day
- » Think Fast, Talk Smart
- » Steve Jobs' Last Words
- » 12 Habits of Extraordinarily Motivated People
- » Writing-up Qualitative Research
- » Bad Boys Blue Collection
- » 우리는 왜 우리가 되지 못하는가?
- » How Google Works
- » What is Visual Thinking?
- » Ordeal
- » Einstein and Buddhism
- » CV vs. Resume
- » You've got to find what you love
- » What You're Really Meant to Do
- » Logotherapy
- » Today
- » Mathematical Universe?
- » The Power of Inquiry to Spark Breakthrough Ideas
- » A More Beautiful Question
- » Categorical Imperative
- » Ballad Music Collection
- » Discussion
- » The School of Athens
- » Common Lisp using LLVM and C++ for Molecular Metaprogramming
- » Big Data Challenges and Opportunities
- » Darwin Day 2015 Questions
- » A well-planned kickoff meeting sets the tone for a successful project
- » Seven Key Elements for a Successful Kickoff Meeting Agenda
- » Cornell Notes
- » Writing An Author Bio
- » How to Prepare for Your Performance Review Meeting
- » Passion
- » Effective Meeting Strategy
- » Research skills
- » Steve Jobs - One Last Thing
- » Camino and Hope
- » Camino, Dream and Choice
- » Creativity in Business
- » A Thousand Dreams of You
- » Such a Man
- » The Selfish Gene
- » 10 Usability Heuristics for User Interface Design
- » Folder Management
- » Computer Science @ University of Cambridge
- » How to Share a Bread Evenly
- » 24 Live Another Day
- » Steve Jobs at SIGGRAPH 1995
- » Chest aching Story
- » Like a star
- » Let it go
- » Randy Pausch Last Lecture, Achieving Your Childhood Dreams
- » Sunflower
- » A Flying Butterfly
- » Impressionist Tutorial
- » Introduction to Impress.js
- » Open The Door
- » How to play the piano in a week
- » Smile Again
- » The Presentation Secrets of Steve Jobs
- » Wisdom from Steve Jobs
publications
- » Conferences 2018
- » Telling a Story
- » The Scope of a Paper
- » How To Read A Paper
- » Paper Writing Strategy
- » Paper Writing
- » How to Read a Technical Paper
- » How to Share a Bread Evenly
- » Stream Data Processing for Performance Evaluation of the Equipment in an Offshore Plant
- » How to write a good research paper
- » How to overcome common obstacles to publishing in English
- » Publications
software engineering
- » Google Developer Day Keynote
- » Work Breakdown Structure (WBS)
- » Software Engineering Principle 3
- » Software Engineering Principle 2
- » Software Engineering Principle 1
- » Unit Test
- » Open Source Analysis Tips
- » Functional Specifications
- » Using Gitlab on Mac OS X
- » Gitlab Installation on Mac OS X
- » Scrum Events
- » Effective Team Membership
- » Sequence Diagram
- » Explaining Agile
- » Real-Time Data Stream Manager (RTDSM) Interface Specification
- » Extreme Programming
- » Scrum Agile Development Process
- » Agile Project Management
- » Agile Processes and Scrum
spark
- » Advanced Analytics with Spark
- » Queries on Compressed RDDs
- » Twitter Analysis with Apache Spark and IBM Watson
- » The Pushdown of Everything
- » A Taste of Random Decision Forests on Apache Spark
- » ADAM Project
- » Spark - The Ultimate Scala Collections
- » BDAS Software Stack
- » Spark Cluster Configuration
- » Building Tachyon Master Branch
- » Creating Applications using Spark Streaming
- » Creating a Spark Applications
- » Working with RDD Operations
- » BlinkDB Review
- » Sparrow Paper Review
- » Spark Architecture Shuffle
- » Explore In-Memory Data Store Tachyon
- » Tachyon Overview
- » AMP Camp 6
- » Spark 1.5 DataFrame API Highlights
- » Project Tungsten
- » SparkR Setting for RStudio
- » SparkR
- » Spark Lab 3
- » Spark Lab 2
- » Diving into Spark Streaming's Execution Model
- » Lambda Architecture
- » Zeppelin Tutorial
- » Introduction to Data Science with Apache Spark
- » The Internet of Things Transforms Transactions with Apache Spark
- » Improving Spark for Data Pipelines with Native YARN Integration
- » BlinkDB User Guide
- » Recent performance improvements in Apache Spark
- » Bringing Spark Closer to Bare Metal
- » Data Exploration Using BlinkDB
- » Programming With SampleClean
- » SampleClean
- » Running BlinkDB Locally
- » Apache Spark - Caching and Checkpointing Under the Hood
- » Introduction to BlinkDB
- » Spark with Python Notebook on Mac
- » Spark Notebook
- » Spark-GPU Cluster Development in a Notebook
- » Deep Dive into Spark SQL’s Catalyst Optimizer
- » Introducing DataFrames in Spark for Large Scale Data Science
- » Introduction to the Spark
- » Big Spatial Data Processing using Spark
- » SpatialSpark
- » New Directions for Spark in 2015
- » The Spark SQL Optimizer and External Data Sources API
- » DataFrame and SQL Operations
- » Design Patterns for using foreachRDD
- » Output Operations on DStreams
- » Transformations on DStreams
- » DStream Sources
- » Discretized Streams
- » Spark Streaming Basic Concepts
- » Spark Streaming Quick Example
- » Spark Streaming Overview
sql on hadoop
- » Lightning Fast Social Media Analytics
- » Statistics-Driven OLAP Acceleration using Query Column Sets
- » Apache Calcite Overview
- » Zeppelin Tajo Interpreter
- » Release Notes - Tajo - Version 0.11.1
- » Rethinking SQL for Big Data with Apache Drill
- » BlinkDB Review
- » Approximate Aggregation Queries in Presto
- » Using Presto to combine data from Hive and MySQL
- » Apache MRQL Installation Instructions
- » Common Misconceptions about SQL on Hadoop
- » Apache MRQL
- » Apache Drill Introduction
- » Teradata Bets Big on Presto for Hadoop SQL
- » Totally Lazy
- » A SQL Query Compiler
- » Best SQL-on-hadoop Tool
stream computing
- » Wide & Deep Learning; Memorization + Generalization with TensorFlow
- » Keynote - TensorFlow Dev Summit 2017
- » Off-heap Memory in Apache Flink and the curious JIT compiler
- » High-throughput, low-latency, and exactly-once stream processing with Apache Flink
- » Overview of the Apache Flink
- » Apache Flink Seminar
- » Lambda Architecture
- » Apache Storm Basics
- » Processing streaming data in Hadoop with Apache Storm
- » Navier-Stokes Equations
- » Theory of Everything
- » Facebook Presto
- » The Real-Time Big Data Architecture (RTDBA) Stack
- » The Five Phases of Real Time
- » Big Data in Motion
- » Continuous Quries Languages
- » Semantics of Relations in Continuous Queries
- » Continuous Quries as Views
- » Continuous Query Semantics and Operators (Part II)
- » Stream Windows
- » Continuous Query Semantics and Operators (Part I)
- » Writing Input And Output Adapters
- » Stream Models
- » Understanding Human Dynamics
- » Esper Relational Database Adapter
- » Event Rendering to XML and JSON
- » Esper Socket Adapter
- » Esper Adapter Concept
- » Data Stream Management
- » Physical Properties and selection of thermodynamic models
- » Centrifugal Compressor Efficiency - Part 4
- » Centrifugal Compressor Efficiency - Part 3
- » Centrifugal Compressor Efficiency - Part 2
- » Centrifugal Compressor Efficiency - Part 1
- » In-Memory Caches
- » Pattern Matching Techniques
- » Monte-Carlo Methods An Introduction
- » PRODML Data Schema and API Specifications
- » The Invention of Stream Computing
- » Real-time Operational Intelligence
- » Introduction to Esper for Java
- » Analyzing Data in Motion A Real-World View
- » Stream Data Schema Design
- » Introduction to Stream Computing
- » Streams Application Design
- » Filter pattern
- » Streams design patterns
- » Thermodynamics Basic Concepts
tez
- » Understanding the world of SQL on Hadoop.
- » Apache Tez Presentation
- » Announcing Apache Pig 0.13.0
- » Benchmarking Apache Hive 13 for Enterprise Hadoop
- » The Stinger Initiative From HortonWorks With Hadoop
- » Big Data & Brews - Ari Zilka, CTO of Hortonworks' Talk
- » Comparing Apache Tez and Microsoft Dryad
- » Apache Spark and Apache Tez
- » Eight SQL on Hadoop Challenges
- » 11 SQL-on-Hadoop Tools
- » Interactive Query for Hadoop with Apache Hive on Apache Tez
- » Introducing Apache Tez 0.4
- » Big Data Genomics Sequencing
- » YARN Framework and Fault Tolerance
- » Apache Tajo
- » Build Instructions for Apache Tajo
- » Hive on Tez
- » Functional Requirements of Hive on Tez Phase I
- » Query Planning of Hive on Tez
- » Design of Hive on Tez
- » Apache Tez 0.3 Released!
- » Tez Design - Introduction
- » Writing a Tez Input, Processor and Output
- » Runtime API in Apache Tez
- » Re-Using Containers in Apache Tez
- » Introducing Tez Sessions
- » Data Processing API in Apache Tez
- » Apache Tez Dynamic Graph Reconfiguration
- » Apache Tez A New Chapter in Hadoop Data Processing
- » Introduction to the Tez
yarn
- » Min-cost Flow Network Algorithms
- » Elasticity with YARN
- » Broadcast Join in Tajo
- » Tajo Cluster Architecture
- » Improving Spark for Data Pipelines with Native YARN Integration
- » What is Mesos?
- » FAQ for the REEF
- » Introduction to REEF
- » Application Interaction with the ResourceManager
- » Resource Manager
- » Build YARN Apps on Hadoop with Apache Slider
- » Apache Hadoop YARN Architecture
- » Apache Spark Resource Management and YARN App Models
- » Development Tools for Big Data
- » Apache Tajo JDBC Driver
- » Apache Tajo HCatalog Integration
- » Spark for Data Science; A Case Study
- » Apache Tajo Backup and Restore
- » Apache Tajo Table Partitioning
- » An Insight into Hadoop Yarn Resource Manager
- » Apache Tajo SequenceFile
- » Apache Tajo Parquet
- » How Apache Hadoop YARN HA Works
- » Apache Tajo RCFile
- » Apache Tajo Table Management
- » Apache Tajo CSV (TextFile)
- » Apache Tajo Functions
- » Apache Tajo SQL Language
- » Apache Tajo Shell
- » Apache Tajo™ 0.8.0 Configuration
- » Apache Tajo™ 0.8.0 Released!
- » Apache Tajo™ 0.8.0 Documentation
- » The Stinger Initiative From HortonWorks With Hadoop
- » Simple YARN Application
- » Apache Hadoop YARN – Related Work
- » Failures in YARN
- » Failures in Classic MapReduce
- » YARN Framework and Fault Tolerance
- » Apache Hadoop YARN – Node Manager (NM)
- » Apache Hadoop YARN – Application Master (AM)
- » Apache Hadoop in 2013 - The State of the Platform
- » Apache Hadoop YARN – Architecture Overview
- » Apache Hadoop YARN – Resource Manager (RM)
- » Apache Hadoop YARN – History and Rationale, Shared Clusters
- » Apache Hadoop YARN – History and Rationale
- » Apache Hadoop YARN – ResourceManager
- » Apache Hadoop YARN – Yet Another Resource Negotiator
- » Apache Hadoop YARN – NodeManager
- » Apache Hadoop YARN – Concepts and Applications
- » Apache Hadoop YARN – Background and Overview
- » Introducing Apache Hadoop YARN
- » YARN (MapReduce 2)
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