LIST OF TAGS
- artificial general intelligence16
- big data683
- computer graphics68
- data management203
- design patterns104
- graph mining385
- liberal arts121
- machine learning1504
- parallel computing64
artificial general intelligence
- » How AI Cracked the Protein Folding Code and Won a Nobel Prize
- » Pre-Training GPT-4.5
- » Marvin Minsky - The Emotion Machine
- » Geoffrey Hinton - Will AI Save the World or End it?
- » Marvin Minsky - Artificial Intelligence
- » Marvin Minsky; A Society of Minds
- » The Society of Mind
- » Diffusion for World Modeling; Visual Details Matter in Atari
- » OpenAI Surprises Everyone, Superintelligence approaches
- » Yann LeCun & John Werner on The Next AI Revolution
- » South Korean President Yoon Suk Yeol removed from office
- » NEW VISUAL CoT Reasoning
- » Causal-aware machine learning
- » Learning to Predict Global Atrial Fibrillation Dynamics
- » The Heterophilic Graph Learning Handbook
- » How to write Tree of Thoughts Prompts
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
- » 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
- » Introduction to Graph Thinking
- » Advanced Analytics with Spark
- » Mining Massive Data Sets
- » 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
- » 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
- » Gunrock
- » 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
- » Min-cost Flow Network Algorithms
- » Elasticity with YARN
- » Knowledge Curation and Knowledge Fusion
- » Self-Curating Databases
- » Lightning Fast Social Media Analytics
- » Statistics-Driven OLAP Acceleration using Query Column Sets
- » 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
- » Zeppelin Tajo Interpreter
- » What is VoltDB?
- » Release Notes - Tajo - Version 0.11.1
- » 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
- » SDN and Big Data
- » Connected Components in MapReduce and Beyond
- » Connected Components in MapReduce and Beyond
- » Analytics in Motion (AIM)
- » Big Data is (at least) Four Different Problems
- » Real-time Big Data Analytics
- » Queries on Compressed RDDs
- » HiBench Suite
- » 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
- » Central Office Re-architected as a Datacenter (CORD)
- » Introducing TPCx-HS Benchmark for Big Data
- » Building LuxRender on OS X
- » Machine Driven, Human Guided Data Curation for Decision Advantage and Action
- » Twitter Analysis with Apache Spark and IBM Watson
- » Rethinking SQL for Big Data with Apache Drill
- » Organizational Charts
- » The Pushdown of Everything
- » Machine Learning as the Key to Personalized Curation
- » Big Data, Fast Data The Need for In Memory Database Technology
- » Arrary DBMS
- » Arrary Databases
- » Michael Stonebraker's Talk
- » Michael Stonebraker's Talk
- » Running Hadoop MapReduce on Tachyon
- » What is HTAP?
- » A Taste of Random Decision Forests on Apache Spark
- » ADAM Project
- » Spark - The Ultimate Scala Collections
- » BDAS Software Stack
- » Basic ONOS Tutorial
- » Spark Cluster Configuration
- » 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
- » BlinkDB Review
- » 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?
- » 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
- » Project Tungsten
- » Building Good Docker Images
- » SparkR Setting for RStudio
- » SparkR
- » Cognitive Computing
- » Spark Lab 3
- » Spark Lab 2
- » Diving into Spark Streaming's Execution Model
- » 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
- » Potential Novel Applications of Open SDN
- » Hybrid Unified Communications and Collaboration
- » Hype Cycle
- » SDN Note
- » RAMCloud Overview
- » RAMCloud
- » Open Network Operating System
- » The Virginian Database
- » The Analytical Bootstrap
- » DAQ Introduction
- » A New Paradigm for Approximate Query Processing
- » Understanding the Differences between SDN and Server Virtualization
- » Future of big data analytics
- » Five most popular similarity measures implementation in python
- » Massive Throughput Database Queries with LLVM on GPUs
- » Open Daylight Technical Overview
- » Hadoop + GPU Architecture
- » Overview of the Apache Flink
- » Apache Flink Seminar
- » Lambda Architecture
- » Apache MRQL Installation Instructions
- » Common Misconceptions about SQL on Hadoop
- » Apache MRQL
- » MapD (Massively Parallel Database) Overview
- » Data Science using GPUs
- » Apache Drill Introduction
- » IBM Juices Hadoop With Java On Tesla GPUs
- » Broadcast Join in Tajo
- » Tajo Cluster Architecture
- » Apache Hadoop vs. IBM Platform Symphony and Infosphere BigInsights
- » How HPC is Hacking Hadoop
- » High-Performance Computing Cluster
- » Zeppelin Tutorial
- » Teradata Bets Big on Presto for Hadoop SQL
- » 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
- » Totally Lazy
- » A SQL Query Compiler
- » Recent performance improvements in Apache Spark
- » Bringing Spark Closer to Bare Metal
- » Best SQL-on-hadoop Tool
- » Data Exploration Using BlinkDB
- » Programming With SampleClean
- » SampleClean
- » Running BlinkDB Locally
- » Large Graph-Mining - Power Tools and a Practitioner's Guide
- » Hadoop-RDMA
- » 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
- » FAQ for the REEF
- » Introduction to BlinkDB
- » Introduction to REEF
- » Spark with Python Notebook on Mac
- » MapReduce for C - Run Native Code in Hadoop
- » Spark Notebook
- » Spark-GPU Cluster Development in a Notebook
- » 5 Ways to Make Your Hive Queries Run Faster
- » 39 Data Visualization Tools for Big Data
- » HIVE 0.14 Cost Based Optimizer (CBO) Technical Overview
- » Deep Dive into Spark SQL’s Catalyst Optimizer
- » Introducing DataFrames in Spark for Large Scale Data Science
- » 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
- » An Introduction To Software Defined Networking
- » 10 ways to query Hadoop with SQL
- » Application Interaction with the ResourceManager
- » Resource Manager
- » Build YARN Apps on Hadoop with Apache Slider
- » 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
- » SDN과 NFV와 네트워크 가상화의 차이점 이해하기
- » Apache Hadoop YARN Architecture
- » 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
- » SIX sparkling features of Apache Spark!
- » Use Parquet with Impala, Hive, Pig, and MapReduce
- » Use Parquet with Impala, Hive, Pig, and MapReduce
- » Actian Unleashes SQL Users on Hadoop with Easy Access, Faster Performance
- » Actian Unleashes SQL Users on Hadoop with Easy Access, Faster Performance
- » Presto as a Service
- » Presto as a Service
- » HIVE as a Service
- » HIVE as a Service
- » Understanding the world of SQL on Hadoop.
- » How to Develop Hadoop Tests
- » MRUnit Tutorial
- » Serialization
- » HIVE TPC-DS Benchmark
- » HIVE TPC-DS Benchmark
- » Distributed Computing with Spark
- » Distributed Computing with Spark
- » Why Oracle Big Data SQL Potentially Solves a Big Issue with Hadoop Security
- » 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
- » In-Stream Big Data Processing
- » Apache Hadoop YARN ResourceManager
- » Apache Hadoop YARN ResourceManager
- » Introduction To Pivotal's Next Generation Analytic Data Lake
- » Introduction To Pivotal's Next Generation Analytic Data Lake
- » War of the Hadoop SQL engines. And the winner is …?
- » War of the Hadoop SQL engines. And the winner is …?
- » Exploring Pivotal HD Single Node (VM)
- » Exploring Pivotal HD Single Node (VM)
- » How Pivotal Works
- » How Pivotal Works
- » Parquet - Columnar Storage for the People
- » Parquet - Columnar Storage for the People
- » Pivotal Big Data Suite
- » Pivotal Big Data Suite
- » Pivotal HD Introduction & Demo
- » Pivotal HD Introduction & Demo
- » Special "Use Cases" Big Data & Brews - Hortonworks, Pivotal, MapR
- » Special "Use Cases" Big Data & Brews - Hortonworks, Pivotal, MapR
- » What is Apache Drill?
- » What is Apache Drill?
- » Drilling into Big Data with Apache Drill
- » Drilling into Big Data with Apache Drill
- » Fun with TPC-H (Part 1)
- » Fun with TPC-H (Part 1)
- » Implementing Drill Math Functions
- » Implementing Drill Math Functions
- » Presto By Facebook – A SQL Engine
- » Presto By Facebook – A SQL Engine
- » Big Data - The Hadoop Data Warehouse
- » Big Data - The Hadoop Data Warehouse
- » Building a Data Warehouse with Hadoop
- » Building a Data Warehouse with Hadoop
- » Why does MapReduce + GPU Computing?
- » Why NoSQL?
- » Apache Spark Documentation
- » Google Cloud Dataflow, a Cloud-native data processing service
- » Datawarehouse
- » 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
- » Big Data Architecture
- » Comparing Information Architecture Operational Paradigms
- » Big Data Architecture Capabilities
- » How to prepare for SDN
- » 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
- » M7 - Native Storage for MapR Tables
- » Hadoop 2.4.0 Installation on Mac OS X Mavericks
- » Software-Defined Networks
- » Future of the Network Documentary
- » A Leap Forward With SQL on Hadoop
- » Special "How it Works" Big Data & Brews
- » GPU Programming for the Data Sciences
- » 10 undeniable reasons people hate Big Data
- » Hadoop Sector will Have Annual Growth of 58% for 2013-2020
- » Why Impala Continues to Lead?
- » Apache Spark Resource Management and YARN App Models
- » Google Spanner
- » Column-Family Stores
- » Key-Value and Document Data 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
- » 5 technologies that will help big data cross the chasm
- » 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
- » Simple YARN Application
- » 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
- » Presto as a Service
- » Apache Hadoop YARN – Related Work
- » Adding ACID to Apache Hive
- » 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
- » Smart Things vs. Many Experienced Things
- » Apache Hadoop YARN – Node Manager (NM)
- » Hardening Hadoop for Healthcare with Project Rhino
- » 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
- » Functional Programming
- » Navier-Stokes Equations
- » Implementing Graph-Based Applications
- » Designing Graph-Based Applications
- » Data-intensive Computing
- » Network Topology and Hadoop
- » Ensuring the Validity, Veracity, and Volatility of Big Data
- » Operationalizing Big Data
- » Modern Oil & Gas Architectures Built with 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
- » NoSQL Databases
- » Visualization for Data-Intensive Science
- » Theory of Everything
- » Stanford Network Analysis Platform(SNAP)
- » Gray’s Laws Database-centric Computing in Science
- » Connecting to the Scientists
- » Big Data Dilemma
- » Polyglot Persistence
- » Community Detection - Clustering Coefficient
- » 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!
- » 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
- » Apache Drill
- » Predictive Policing
- » A Taxonomy of Predictive Methods
- » Apache Hadoop YARN – History and Rationale
- » “Big Data” Promises a Revolution
- » 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
- » Introducing Apache Hadoop YARN
- » 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
- » Key Points from NoSQL Distilled
- » Multi-tenant Database Systems
- » Big Data in Motion
- » Tez Design - Introduction
- » 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
- » Solving the Wanamaker Problem for Healthcare
- » Continuous Query Semantics and Operators (Part II)
- » Stream Windows
- » Continuous Query Semantics and Operators (Part I)
- » Parallel R
- » Writing Input And Output Adapters
- » Stream Models
- » Understanding the Types of Big Data Problems
- » Understanding Human Dynamics
- » Esper Relational Database Adapter
- » Event Rendering to XML and JSON
- » Esper Socket Adapter
- » Esper Adapter Concept
- » 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
- » Principal Component Analysis
- » 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
- » Explaining Agile
- » Introduction to Esper for Java
- » The Emergence of NoSQL
- » Real-Time Data Stream Manager (RTDSM) Interface Specification
- » Modelling in Big Data
- » Analyzing Data in Motion A Real-World View
- » Understanding trade-offs with Brewer’s CAP theorem
- » Applications using graph representation
- » YARN (MapReduce 2)
- » Stream Data Schema Design
- » Introduction to Stream Computing
- » Streams Application Design
- » Analytics vs. Predictive Analytics
- » Streams design patterns
- » The evolution of analytics
- » Extreme Programming
- » Thermodynamics Basic Concepts
- » Scrum Agile Development Process
- » Impedance Mismatch
- » The Value of Relational Databases
- » What is NoSQL?
- » Neo4j
- » Graph Databases
- » 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
- » 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
- » Visualization for Data-Intensive Science
- » A Material Point Method For Snow Simulation
- » Reflections on Image-Based Modeling and Rendering
- » Korean Computer Graphics Conference 2013
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
- » Breakthrough Processor and System Design with SPARC M7
- » Life Beyond Column-Stores - Exploiting intra-cycle parallelism
- » OLTPBench
- » CrowdSky; Skyline Computation with Crowdsourcing
- » Efficient Computation of Containment and Complementarity in RDF Data Cubess
- » Data Processing in Modern Hardware
- » 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
- » 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
- » PostgreSQL
- » Vagrant Tutorial
- » Introduction to Vagrant
- » A Hands-free Adaptive Store
- » The Bw-Tree Key-Value Store
- » Bitmap Indices
- » IBM Cognos Analytics
- » Main Memory Databases
- » Dictionary-based Order-preserving String Compression
- » The Merits of Data Compression
- » DBMS Components
- » Latches, Spinlocks, and Lock Free Data Structures
- » Database Systems Course
- » Gartner Top 10 Tech Trends for 2016
- » CES 2016 Keynote
- » In-Memory Accelerator for Hadoop
- » Building Postgresql on OS X
- » TPC-H
- » Setting Up An Organized Folder Structure
- » TPC-DS
- » In-Memory Data Management Course
- » Databaseology Lectures
- » Zeppelin Deployment
- » Zeppelin Structure
- » 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
- » Optimizing Big Data Through Curation
- » Cognitive Computing
- » SAP S4 HANA Technical White Board
- » The Challenges of Data Science
- » How Data Science Driven Software is Eating the Connected World
- » How WellCare Accelerated Big Data Delivery to Improve Analytics
- » What is Hybrid Transaction/Analytical Processing (HTAP)?
- » Eight Big Trends in Big Data Analytics
- » My Top 10 Assertions about Data Warehouses
- » 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?
- » 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
- » Building the Health Information Infrastructure for the Modern ePatient
- » Modelling in Big Data
- » VLDB 2013
design patterns
- » Notes on Graph Theory
- » Visual Studio Code, CMake and LLDB
- » Extreme Team
- » Visual Studio Code - C++ Development
- » UML Tools for Python
- » Introduction to gRPC
- » Design Patterns in Python
- » Sequential Implementation of Mesoscopic Simulation
- » Limitations of Macroscopic and Mesoscopic Simulation
- » Simulation Loop
- » Mesoscopic Simulation
- » Traffic Prediction
- » Apache Mesos
- » Distributed Systems
- » High-Performance Mesoscopic Traffic Simulation
- » Microscopic Simulation
- » Macroscopic Simulation
- » Traffic Simulation Background
- » T tree
- » Cellular Automata
- » Pair Programming Tips
- » Computability, Complexity, and Theory - Georgia Tech
- » Google Developer Day Keynote
- » In-core computation with HyperBall
- » 소프트웨어 개발의 이해를 돕기 위한 비유
- » Redmine Plugin Installation
- » Work Breakdown Structure (WBS)
- » Wearable Technology Trends in 2016
- » Software Engineering Principle 3
- » Software Engineering Principle 2
- » Software Engineering Principle 1
- » 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
- » 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)
- » Dependency Injection
- » GNUPlot Basics
- » Google Protocol Buffer Basics - Java
- » 11 Core Big Data Workload Design Patterns
- » How to fix “System program problem detected” error on Ubuntu
- » Samsung Developer Day 2014 at MWC - Samsung MultiScreen SDK
- » Unit Test
- » Open Source Analysis Tips
- » Functional Specifications
- » Burndown Chart
- » Bigtable
- » Chubby
- » The Google File System (GFS)
- » PageRank Algorithm
- » Overview of Data Storage and Coordination Services
- » Underlying Communication Paradigms
- » PageRank History
- » Overall Architecture and Design Philosophy
- » Bloom Filter
- » Using Gitlab on Mac OS X
- » Gitlab Installation on Mac OS X
- » Why Functional Programming?
- » Macport Commands Summary
- » Wiki Tutorial
- » Scrum Events
- » Introduction to the Maven
- » Native C/C++ Like Performance For Java Object Serialization
- » Using ByteBuffer in Java
- » Effective Team Membership
- » Confluent and Functional Persistence
- » Full Persistence
- » Partial Persistence
- » Persistent Data Structures
- » 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
- » Sequence Diagram
- » JAXB Class Generation From XML Schema File using JAXB Tool
- » My Scoop It! Sites
- » Concurrent Bug Patterns and How to Test Them
- » Introduction to Bug Patterns in Java
- » Foundational data architecture patterns
- » N-Screen Application Development Manual
- » Android WebView Application using Naver Open API
- » Filter pattern
- » Agile Project Management
- » Agile Processes and Scrum
- » Git Commands
- » Git Concepts and Architecture
- » Adding a post to github page
graph mining
- » Converging Advances to Accelerate Molecular Simulation
- » Learning Delaunay Surface Elements for Mesh Reconstruction
- » 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
- » MapGraph
- » Stanford Network Analysis Platform(SNAP)
liberal arts
- » Korea Summit 2018
- » Why we learn?
- » How To Read a Scientific Research Paper
- » How to Write a Research Plan
- » Just Do It
- » Conferences 2018
- » 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
- » Automatic Source Transformation from C++ to CUDA using Clang/LLVM
- » 그댄 행복에 살텐데
- » The 5 Leadership Styles
- » 등록금 0원
- » Emotional Intelligence
- » Studying in Germany
- » 민물장어의 꿈
- » Research Topics in Database Management
- » IEEE NetSoft 2016 Call for Demonstrations
- » Sparks of Genius
- » Papers in Parallel and Distributed Data Management
- » If today were the last day
- » Telling a Story
- » The Scope of a Paper
- » Map-D
- » Main Memory and Streaming Databases
- » Think Fast, Talk Smart
- » Calendar of Computer Graphics Events
- » LaTeX Fonts on Mac OS X
- » Proceedings of the VLDB Endowment, Volume 9
- » Research Posters 101
- » A GPU Parallelized Database Accelerator
- » Steve Jobs' Last Words
- » 12 Habits of Extraordinarily Motivated People
- » Writing-up Qualitative Research
- » Bad Boys Blue Collection
- » How To Read A Paper
- » Paper Writing Strategy
- » Paper Writing
- » ACM TODS Manuscript Preparation
- » 우리는 왜 우리가 되지 못하는가?
- » Distributed Computing Conferences
- » IEEE Transactions on Knowledge and Data Engineering
- » How Google Works
- » Summarizing
- » How to Write a Good Paper
- » 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
- » How to Read a Technical Paper
- » SIGMOD 2015 Papers
- » 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 Write a Paper Efficiently
- » 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
- » 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
- » Chest aching Story
- » Like a star
- » TDD Best practices
- » Test-Driven Development in a nutshell
- » Let it go
- » Randy Pausch Last Lecture, Achieving Your Childhood Dreams
- » Sunflower
- » A Flying Butterfly
- » Synchronization among Heterogeneous Distributed Objects
- » How to write a good research paper
- » Impressionist Tutorial
- » Introduction to Impress.js
- » How to overcome common obstacles to publishing in English
- » Publications
- » Open The Door
- » Characteristics of the Smart Devices
- » How to play the piano in a week
- » Smile Again
- » Logical Communication-based Middleware for Collaboration among Smart Apps in P2P Network Environments
- » The Presentation Secrets of Steve Jobs
- » Paper Organization
- » Wisdom from Steve Jobs
machine learning
- » Deep Learning for Protein Engineering Primer
- » Language Modeling from Scratch
- » Distilling Foundation Models via Energy Hessians
- » Inference Scaling; A New Frontier for AI Capability
- » Mixed-modal Language Modeling
- » Reducing the Dimension of Language
- » 5 Unbelievably Useful AI Tools For Research in 2025
- » Why Intelligent People Scare Society
- » Google’s New AI Is Recreating the Whole World ...
- » Boltzmann Machines - Goeffrey Hinton
- » Accelerating Scientific Discovery with AI
- » How Deep Research Works
- » How to Live an Asymmetric Life
- » 4o Image Generation in ChatGPT and Sora
- » WTF is Artificial Intelligence Really?
- » Accelerating Scientific Discovery with AI
- » AI should NOT be regulated at all! - Domingos
- » Bayesians and Analogical Learning in AI
- » The Symbolist Approach to AI
- » Mathematical Obstacles on the Way to Human-Level AI
- » Inverse problems with experiment-guided AlphaFold
- » Why Can't AI Make Its Own Discoveries? — With Yann LeCun
- » How DeepSeek changes the LLM story
- » Why it Matters That Babies and Language Models are ...
- » Knowledge is structured and domain-specific
- » Rules vs. Neurons and what may be next
- » Why Physical AI Will Arrive Before AGI?
- » Neuroscience and AI; a symbiosis
- » Nvidia's GTC 2025 Keynote
- » How to Develop Teams of AI Agents with NVIDIA AgentIQ
- » François Chollet on OpenAI o-models and ARC
- » Cosmos World Foundation Model Explained
- » Huge Language Models and Neuro-Symbolic AI
- » Rethinking Recommender System's Sustainability
- » The Next Wave of AI - Physical AI
- » Causality for NLP Reading List
- » Group Relative Policy Optimization(GRPO)
- » Measuring Multimodal Reasoning with the MMMU Benchmarks
- » Generative Modeling
- » How Intelligent Systems Are Transforming Business
- » Can LLMs Achieve Casual Reasoning and Cooperation?
- » Father of AI - AI Needs PHYSICS to EVOLVE
- » Augmenting Causal Discovery with LLMs
- » Future of AI and Games
- » Weakly supervised causal representation learning
- » NVIDIA CEO Jensen Huang Keynote at CES 2025
- » Designing Advanced AI Agent Architectures for Intelligent Systems
- » Large Language Models in 2025
- » Knowledge Graph or Vector Database… Which is Better?
- » Fixing RAG with GraphRAG
- » Scaling AV Data With Omniverse and Cosmos
- » Build Custom World Foundation Models with NVIDIA Cosmos
- » Causality Upside Down
- » Intro to GraphRAG
- » Reproducibility Study of Explaining Temporal Graph Models
- » Weakly Supervised Causal Representation Learning
- » Neural Radiance Fields
- » What is DeepSeek? AI Model Basics Explained
- » Question Generation using Deep Learning in NLP
- » Causal Machine Learning in Python
- » R1, OpenAI’s o3, and the ARC-AGI Benchmark
- » Which Agentic AI Framework to Pick?
- » Time and Space in the Brain
- » Towards Human-like Educational Question Generation with LLMs
- » Chatbots vs AI Assistants - Key Differences
- » The Shape of AI to Come!
- » Cohort Construction and Refinement
- » A Path Towards Autonomous Machine Intelligence
- » What is Agentic RAG?
- » Implications of Artificial General Intelligence
- » Demystifying Long Chain-of-Thought Reasoning in LLMs
- » Potential Outcome Framework and Structured Causal Models
- » AI Trends for 2025
- » Non-linear Causal Discovery for Addtive Noise Models
- » Building Knowledge Graphs with LLM Graph Transformer
- » Financial Machine Learning - A Practitioner’s Perspective
- » Causal Hierarchy Theorem
- » Backdoor Criterion
- » Covariance-based Causal Discovery
- » Actual Causality - A Survey
- » Progress in Agentic AIs - Sakana AI
- » Interpretable Deep Learning for New Physics Discovery
- » Causal-aware Reasoning
- » When AI meets Computational Fluid Dynamics
- » Machine Learning on Function Spaces
- » Review - Symbolic Regression
- » OpenAI o1
- » Ontologies, Graph Deep Learning, & AI
- » Deep Symbolic Regression
- » Biophysics-Informed Neural Networks
- » Physics Informed Machine Learning through Symbolic Regression
- » Unlocking GenAI with GraphRAG & Knowledge Graphs
- » Step-by-step Code for Knowledge Graph Construction
- » Graph RAG - Improving RAG with Knowledge Graphs
- » How to Build Knowledge Graphs With LLMs
- » PINNs - Ordinary Differential Equations Tutorial
- » Scaling LLM Test-Time Compute
- » Introduction to Deep Research
- » PIML and Kolmogorov-Arnold Networks (KANs)
- » Symbolic Regression with Transformers
- » On Learning Heterogeneity and Non-Spuriousness in Causal Graphs
- » Physics-Informed Machine Learning for Engineering Applications
- » Large World Model for Large Context MultiModal LLM
- » Advances in Causal Representation Learning Discovery
- » Agentic AI - A Progression of Language Model Usage
- » Agents Thinking Fast and Slow - A Talker-Reasoner Architecture
- » Understanding Learning and Computation through Symmetry
- » Causal Representation Learning in the Context of Perturbation Screens
- » Unifying LLMs and Knowledge Graphs
- » Causal Discovery & Representation Learning
- » Bridging Machine Learning and Physics
- » A Connection between Probability, Physics and Neural Network
- » Causal Representation Learning - Advances and Perspective
- » Beyond Transformers - Intro to RWKV Architecture & The World To
- » Large Concept Models (LCMs)
- » RAG with a Neo4j Knowledge Graph
- » OpenAI o3, DeepSeek-V3, and the Brundage/Marcus AI bet
- » Learning operators using deep neural networks for multiphysics ...
- » 10 Things Coming In 2025 (A.I In 2025 Major Predictions)
- » Bayesian symbolic regression and the learnability
- » Deep End-to-End Causal Inference
- » Top Strategic Tech Trends for 2025
- » Open AI's Q* Is BACK! - Was AGI Just Solved?
- » Deep End-to-end Causal Inference
- » Physics of Intelligence for Trustworthy and Green AI
- » Can AI disover new physics?
- » LLMs for Causal Inference
- » Causal Representation Learning & Optimal Intervention
- » Decoding eCommerce- Why Causal AI adds unique value
- » Introduction To Causal Inference And Directed Acyclic Graphs
- » Physics-Informed Neural Networks and Verification
- » Physics-Informed Neural Networks - Limitations and Extensions
- » An Introduction to Causal AI
- » Evaluating RAG Performance with Vector Databases
- » Causal Representation Learning
- » XAI meets Natural Language Processing
- » The New Science of Cause and Effect
- » Microsoft GraphRAG - The Best RAG + KG Architecture
- » What is the BLEU metric?
- » Approximating functions, functionals and operators
- » From Large Language Models to Reasoning Language Models
- » Large Language Models and Causal AI
- » Reasoning Using Large Language Models
- » Towards reasoning in Large Language Models - A survey
- » Graph-of-Thoughts (GoT) for AI reasoning Agents
- » Causality - Counterfactuals
- » Building Causal AI Products for Effective Decision-Making
- » NVIDIA CEO Jensen Huang Keynote at CES 2025
- » Causal inference and LLMs; A New Frontier
- » Causality at the Intersection of Simulation, Inference, Science
- » Large language models (LLMs) and Causality
- » Introduction To The MITRE ATT&CK Framework
- » Causal Parrots; Large Language Models May Talk Causality
- » Causal Inference and Large Language Models
- » What's Next for AI in 2025? (Superintelligence Ahead)
- » OpenAI Just Revealed They ACHIEVED AGI (OpenAI o3 Explained)
- » How Do We Build a General Intelligence?
- » Current State of AI Reasoning
- » Artificial General Intelligence (AGI)-Level Reasoning
- » Future of AI and Games
- » Forest-of-Thoughts; AI Test-Time Compute Reasoning
- » AGI Alignment Approaches
- » AGI Alignment Techniques
- » AGI Alignment - Ensuring AGI Meets Various Needs
- » The Future of AGI Systems
- » Cost and Efficiency
- » Inference Techniques
- » Decentralization, Training Dynamics and Scaling
- » Efficient Fine-tuning LLMs with PEFT and LoRA
- » Large-Scale Training
- » Beyound Transformers
- » Model Compression
- » Causal AI for Decision Making
- » Seminar with Professor Geoffrey Hinton
- » What is Causal AI?
- » Artificial General Intelligence (AGI) Levels
- » AI Interfaces to Digital World
- » AI-Level Metacognition
- » Expressive Power of Temporal Message Passing
- » RELBENCH; A Benchmark for Deep Learning on Relational Databases
- » A Benchmark for Learning on Temporal Knowledge Graphs
- » Sutras for Strategic Autonomy in AI
- » Ollama Course – Build AI Apps Locally
- » LangChain Tutorial
- » GraphRAG; LLM-Derived Knowledge Graphs for RAG
- » Hugging Face Course
- » Awesome AGI Survey
- » We’re Close to Artificial General Intelligence
- » The Man Who Predicted AGI Decades Ago
- » AI-Ready Data. What Does That Mean!?
- » How Far Are We From Achieving AGI?
- » 20 Emerging Technologies That Will Change The World
- » Artificial General Intelligence by 2030?
- » 8 Use Cases for Artificial General Intelligence
- » Training Exact Ambient Diffusion Models with Noisy Data
- » Visualizing transformers and attention
- » LangChain vs LangGraph; A Tale of Two Frameworks
- » Principles of Macroeconomics
- » Large Bipartite Subgraph
- » Dynamic Deep Learning
- » Towards Autonomous 6G Networks and the Role of Advanced ML
- » From Multimodal LLM to Human-level AI
- » 6G Networks - A New Era of AI and Machine Learning
- » Making Temporal Betweenness Computation Faster and Restless
- » Multimodal Machine Learning
- » A Brief Introduction to Causal Inference
- » Intro to Quantum Gibbs Samplers - Algorithmic Ingredients
- » Long Range Propagation on Continuous-Time Dynamic Graphs
- » Graph Neural Networks and Spectral Graph Theory
- » Maximum Entropy Method
- » What is Agentic AI?
- » Moravec's Paradox
- » Invariant Learning
- » How Could Machines Reach Human-Level Intelligence?
- » Bayesian Inference - Parametric Inference, Predictive Inference
- » Decoding on Graphs; Empower LLMs with KGs
- » Data Heterogeneity Analysis for Out-of-Distribution Generalization
- » Generative Retrieval
- » Change of Variable Rule
- » Chernoff and Hoeffding bounds
- » Spectral Graph Theory
- » Multimodal Large Language Models
- » RL Loses Against Multi-Agent Intelligence
- » DuckDB in the wild
- » How to transform tabular data into a graph
- » Isoperimetric Number
- » Converting a Tabular Dataset to a Graph Dataset for GNNs
- » Towards Neuro-Symbolic AI with Knowledge Graphs and LLMs
- » Toward Understanding In-context Learning
- » On large language models and transformers
- » Understanding and Improving Efficient Language Models
- » The Hierarchy of Needs for Training Dataset Development
- » Uplift Modeling; From Causal Inference to Personalizations
- » Unifying Graph Neural Networks
- » LLMOps and FMOps for e-commerce
- » What Are Drifts and How to Detect Them?
- » Deep Learning of Dynamics and Coordinates with SINDy Autoencoders
- » Introduction to Llama-agents Framework
- » Building Trusted AI with LLMs
- » How Domain-Specific AI Agents Will Shape the Industrial World
- » Harvard Presents NEW Knowledge-Graph AGENT (MedAI)
- » ParadeDB – Postgres for Search and Analytics
- » Re-thinking Transformers
- » XAI for LLMs; looking under the hood of Large Language Models
- » Physics-informed Machine Learning for Inverse Problems
- » Efficient Gaussian Splatting
- » Evaluating AI models for structure-based drug design
- » Discrete Flow Matching
- » Understanding Large Action Model (LAM)
- » How I Understand Flow Matching
- » Toward a better Deep Learning
- » Geometric Probabilistic Models
- » Fine-tuning and Merging LLMs
- » Normalizing Flows
- » Improving and Generalizing Flow-Based Generative Models
- » Flow Matching; Simplifying and Generalizing Diffusion Models
- » A Fast, Embeddable, Modular Analytic Query Engine
- » Improving equality and productivity with AI-driven tax policies
- » Scaling Limits of Neural Networks
- » Economic Policy Design using DRL and AI Simulations
- » AI Economist - How can AI FIX taxes?
- » Economics and Probabilistic Machine Learning
- » Rethinking Physics Informed Neural Networks
- » Differential Equations and Dynamical Systems
- » High Level Overview of AI and ML in Science and Engineering
- » A Trip Down the Generative Graphics Pipeline
- » Decoding Google Gemini with Jeff Dean
- » A Brief Introduction to Neural Radiance Fields
- » SGD and Weight Decay Secretly Compress Your Neural Network
- » Morris Approximate Counter
- » Towards Understanding the Implicit Regularization Effect of SGD
- » Language Models as World Models
- » Building LLMs from the Ground Up
- » Marginal-based Methods for Differentially Private Synthetic Data
- » Differentially Private Synthetic Data via Foundation Model APIs
- » GraphRAG; The Marriage of Knowledge Graphs and RAG
- » Decoding the Human Brain – A Neurosurgeon’s Experience
- » ML for High-Performance Climate and Earth Virtualization Engines
- » How To Read AI Research Papers Effectively
- » Building Large Language Models (LLMs)
- » DuckDB – Overview and latest developments
- » Unreasonably Effective AI with Demis Hassabis
- » The COMPLETE TRUTH About AI Agents
- » The Future of Knowledge Assistants
- » Responsible AI development
- » The Reparameterization Trick
- » How to Read Machine Learning Research Papers
- » A tutorial on on neurosymbolic techniques
- » Analysis by Synthesis
- » The tip and the iceberg; deep learning and embodiment
- » Generating Synthetic Tabular Data That’s Differentially Private
- » TableDiffusion; Generative AI for private tabular data
- » DeepJoin Joinable Table Discovery with Pretrained Language Models
- » Synthetic data generation with CTGAN
- » Retrieval and Re-Reranking
- » What Does Machine Learning Have to Offer Mathematics?
- » What do tech pioneers think about the AI revolution?
- » The Age of AI, Eric Schmidt
- » Decoding the Decoder LLM without de code
- » How to improve your writing
- » The Potential for AI in Science and Mathematics
- » Introduction to Self-Driving
- » Towards Foundation Models for Graphs
- » Energy Based Models
- » Awesome-Graph-LLM
- » Spark Tutorial For Beginners
- » Meta AI, Limits of LLMs, AGI & the Future of AI
- » Analyze Data with LLMs and Python
- » DuckDB Spatial; Supercharged Geospatial SQL
- » Robust Distortion-free Watermarks for Language Models
- » Space is a latent sequence; A theory of hippocampus and PFC
- » Privacy in Web Advertising - Analytics and Modeling
- » Mining Temporal Networks
- » Multimodal Pretraining and Generation for Recommendation
- » Text Attributed Graph Representation Learning
- » Curriculum Learning Theories, Approaches, Applications
- » Neural Contextual Bandits for Personalized Recommendation
- » Large Language Models for Recommendation
- » Data Drift, Quality, Bias and Explainability
- » Mining Exploratory Queries for Conversational Search
- » Recent Advances in Image Generative Foundation Models
- » Sublinear time algorithms in learning
- » A Bi-metric Framework for Fast Similarity Search
- » Vector Search and Embeddings in Organizations
- » Modality Alignment for Multimodal Perception
- » Multimodal Large Language Model Tutorial
- » Probability for Computer Scientists
- » AGI Levels by OpenAI, Grok 2 and Neuralink Update
- » Large Multimodal Models
- » Geometric Deep Learning; From Euclid to Drug Design
- » Algorithmic Discrepancy Theory and Randomized Controlled Trials
- » Continual Learning Beyond Catastrophic Forgetting
- » Project Kuiper; Fast, Affordable Broadband
- » AI for 5G Advanced Toward 6G
- » AI & Machine Learning for Satellite Communications with SES
- » AI for 3D Generation
- » AI for Content Creation
- » Make self-adjusting data structures concurrent
- » Privacy Preserving ML with Fully Homomorphic Encryption
- » Workshop on Urban Scene Modeling
- » KDB.AI | Introduction to Vector Search
- » Multimodal Learning on Graphs
- » From Multimodal LLM to Human level AI
- » Foundation Models for Autonomous Systems
- » Using LLM Agents with Llama 3, LangGraph and Milvus
- » Building Intelligent RAG Applications
- » Learn PostgreSQL fast! | PostgreSQL for beginners
- » The Influence of Computation on Inference
- » Machine learning models of differential gene expression
- » Recent Advances in Vision Foundation Models
- » Illuminating protein space with generative models
- » On the Generalization Capability of Temporal Graph Learning Algorithms
- » Spectral Graph Neural Networks
- » Audio-visual self-supervised baby learning
- » From Higher-Order Graph Structures to Link Prediction and Beyond
- » History and Future of AI at Data + AI Summit 2024
- » How Can Privacy Exist in a Data-Driven World?
- » Can Learned Indexes be Built Efficiently?
- » The Platonic Representation Hypothesis
- » programming ≠ coding
- » SIGMOD 2024 - Alibaba Cloud PolarDB-MP
- » Python RAG Tutorial with Local LLMs
- » Learn RAG From Scratch
- » PostgreSQL as a Vector Database
- » Kernels for Graphs
- » Milvus 101- Most Advanced Vector Database
- » Vector Similarity Search & Indexing Methods
- » Combine Your Data with LLMs with Advanced Search
- » Learn How to Build Multimodal Search and RAG
- » Many shades of Machine Learning Acceleration
- » Charm++ and Adaptive MPI
- » GenAI in Life Sciences Summit
- » Grounding for Gemini with Vertex AI Search and DIY RAG
- » HPC and AI/ML; A Synergistic Relationship
- » Multimodal Retrieval-Augmented Generation with Gemini
- » What is LangChain?
- » PyTorch on Apple Silicon
- » MLP vs KAN, Math, B-Splines, Universal Approximation Theorem
- » Kolmogorov-Arnold Networks VS Regular Deep Learning
- » Kolmogorov-Arnold Networks
- » A Causal Inference Framework for Combinatorial Interventions
- » Introducing GPT-4o with Large Language Models
- » Zero-Shot Relational Learning on Temporal Knowledge Graphs with LLMs
- » Continuous-time Graph Representation with Sequential Survival Process
- » Topological Representations for Temporal Graph Property Prediction
- » LangGraph 101; it's better than LangChain
- » Deep Learning Under Distribution Shift
- » Automating Enterprises With Foundation Models
- » Score Based Models
- » Quantum Computing in Practice
- » DRO and Its Applications in Communication and Networking
- » Data-driven and Distributionally Robust Optimization and Applications
- » Mathematical Foundations of Robust and DRO
- » Introduction to Distributionally Robust Optimization
- » Fundamentals of Intelligent Reflecting Surfaces
- » Multimodal language models for mapping the genotype-phenotype relationship
- » Vector Embeddings Tutorial
- » Vector Database 101 - A Crash Course
- » LLM and Vector Databases - Concepts, Architectures, and Examples
- » Vector Databases Explained
- » Vector Database Fundamentals
- » Indexing in Vector Databases
- » Complete Tutorial on Vector Database
- » Uncovering and Inducing Interpretable Causal Structure
- » GNNs in Computational Biology
- » EVO - DNA Foundation Models
- » Bayesian Causal Inference & Propensity Scores
- » Understanding Bayesian Statistics Without Frequentist Language
- » 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
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