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information technology
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computer science
- What's the difference between programming and coding
- Cross-Platform Data Lineage with OpenLineage
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- PFNs; Use neural networks for 100x faster Bayesian predictions
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- 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
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- 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
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- Data Science–A Systematic Treatment
- LEARN ENGLISH with STEVE JOBS
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- Generative Models for Language and Vision
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- Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
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- Microsoft's Products Will Soon Access Open AI Tools Like ChatGPT
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- Hands-on Explainable Recommender Systems with Knowledge Graphs
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- Open-Source Systems for Federated Learning
- An Introduction to Federated Computation
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- On Future of the Modern Data Stack
- Data Virtualization in Data Fabric
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- The Essential Guide to Data Lineage
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- Introduction to Data Mesh
- PostgreSQL vs MySQL
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- Deep-Dive into Delta Lake
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- Advancing Spark - The Photon Whitepaper
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- Five Things to Consider About Data Mesh and Data Governance
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- Meshing About with Databricks
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- Knowledge Graphs Seminar
- Data Cataloging with Knowledge Graphs
- The Foundation of a Data Fabric
- Data Mesh, Data Fabric, Data Lakehouse
- Tractable Probabilistic Circuits
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- Diffusion Probabilistic Modelling of Protein Backbones in 3D
- ICCV Authors Guidelines
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- Deep Energy-Based Learning
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- Diffusion probabilistic modelling of protein backbones in 3D
- Contrastive Learning in PyTorch
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- Deploy Custom Python Model Server
- Contrastive Learning in PyTorch
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- 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
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- Reinforcement Learning via Sequence Modeling
- Recipe for a General, Powerful, Scalable Graph Transformer
- Spatial Hash Grids & Tales from Game Development
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- Synopses - Samples, Histogram, Wavelets and Sketches
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- A Database Tuning Tool that "Reads the Manual"
- Rethinking Graph Transformers with Spectral Attention
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- On Recoverability of Graph Neural Network Representation
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- How to Serve PyTorch Models with TorchServe
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- On Machines that can Learn Continually
- Introduction to Continual Learning
- Introducing OpenAPI Generator
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- Protein structure prediction with AlphaFold
- Deep Learning for Scientific Computation
- Started with AI in Drug Discovery
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- Graph Ordering Attention Networks
- Introduction to KServe
- Position-Aware Graph Neural Network using Reachability Estimations
- Isomorphism Using Adjacency Matrix
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- Toward Verified Artificial Intelligence
- Modern Data Stack
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- KServe (Kubeflow KFServing) Live Coding Session
- Workflow for ML Projects — MLOps
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- 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
- 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
- Research Resources for Approximate Query Processing
- Research Directions for Sketching Algorithms
- The Forward-Backward Embedding of Directed Graphs
- The Dynamics of Active Sensing in Social Networks
- Large-scale Graph Representation Learning
- Graph Embedding
- Individual and Collective Graph Mining
- New Machine Learning Tool Tracks Urban Traffic Congestion
- Network Analysis
- Getting Started With Network Datasets
- Bourgain's Embedding
- High Accuracy Protein Structure Prediction Using Deep Learning
- Testing Correlation of Unlabeled Random Graphs
- Designing Effective Scientific Presentations
- Quick & Simple Call Graphs in Python
- Latent Network Summarization
- Asymptotic Spectral Distributions for Strongly Regular Graphs
- 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
- Network Representation Learning
- Non-Volatile Memory for Database Management Systems
- Related Papers on Graph Neural Networks
- Finding a New Knowledge
- Beautiful Elastic Simulations, Now Much Faster!
- Approximate Nearest Neighbor Search in High Dimensions
- Billion-scale Approximate Nearest Neighbor Search
- Spatio-Temporal Traffic Prediction Using Deep Learning
- Machine Learning Dissertations
- Research Day
- CS Theory Toolkit
- Deep Learning with PyTorch
- Graph Signal Processing
- Deep Learning Lectures
- Convolutional LSTMs for Video Prediction
- Research Proposal
- Nonlinear Dimensionality Reduction
- What Data Platforms Can Do for Machine Learning Workloads
- AI for Neuroscience and Neuroscience for AI
- Optimal Join Algorithms meet Top-k
- Machine Learning Projects Against COVID-19
- Antistrong Digraphs
- Reflections from the Turing Award Winners
- Recent Progress in High-Dimensional Learning
- Dynamics of Real Networks - Patterns and Algorithms
- Chasing the COVID-19 Pandemic through Modeling
- This AI Removes Shadows From Your Photos!
- Deep Generative Models for Brain Reading
- Setting Up Your Kubeflow ML Environment
- Statistical Learning Theory for Modern Machine Learning
- Deep Learning for Computer Vision
- Big Ideas in Causality and Machine Learning
- AI for physics & physics for AI
- A Blueprint of Standardized and Composable Machine Learning
- Random Walks on Directed Graphs
- Neural Ordinary Differential Equations
- A Spatio-Temporal U-Network for Graph-structured Time Series Modeling
- Variational Graph Recurrent Neural Networks
- Influence Analytics in Graphs
- Learning from Limited Labeled Data
- State of the Industry of Machine Learning Platforms
- Fundamentals of NumPy
- Fundamentals of PyTorch
- Towards Lifelong Learning Machines (L2L)
- Graph Attention Networks
- Learning Equivariant and Hybrid Message Passing on Graphs
- Adversarial Examples and Human-ML Alignment
- Graph Nets; The Next Generation
- Meta-learning
- Meta-learning of Optimizers and Update Rules
- Efficient Deep Learning with Humans in the Loop
- Generalizable Autonomy for Robot Manipulation
- Conditional Channel Gated Networks for Task-Aware Continual Learning
- Transfer/Low-Shot/Semi/Unsupervised Learning
- Mathematical Modeling of Epidemics
- How Behavior Spreads
- Network Diffusion & Contagion
- Explainable AI - Methods, Applications & Recent Developments
- Graph Representation Learning
- The Feature Store
- An Introduction to Meta Learning
- Explainable AI
- Design of BigQuery ML
- Improving Generalization by Self-Training & Self Distillation
- Neurosymbolic AI
- Task dependent adaptive metric for improved few-shot learning
- Variational Continual Learning
- Lifelong and Continual Learning
- The Power Of BI (Business Intelligence) in Digital Marketing
- Urban Computing
- Urban Computing with Advanced Visualization
- IEEE ITEC Best Presentation Award
- Transfer Learning - Part II
- Transfer Learning - Part I
- DeepWalk-Turning Graphs Into Features via Network Embeddings
- Graph Embeddings
- Deep Learning on Graphs
- Hybrid Grains
- Graph Signal Processing
- Learning to Learn
- Exploring And Attacking Neural Networks With Activation Atlases
- Miracles of Algebraic Graph Theory
- A History of Spectral Graph Theory and its Applications
- The JPEG Compression Algorithm
- Transfer Learning
- Geometric Deep Learning Papers
- Controlling Traffic with Reinforcement Learning
- Ian Goodfellow-Generative Adversarial Networks (GANs)
- Notes on Graph Theory
- Metric Learning and Manifolds
- Awesome Knowledge Distillation
- The Power of Self-Learning Systems
- Variational Inference-Foundations and Modern Methods
- 3D Deep Learning
- Machine Learning on Geometrical Data
- Hough Transform for Lines
- Deep Compression
- Model Compression Papers
- Deep RL Bootcamp
- Natural Policy Gradients, TRPO, PPO
- Deep Learning for Recommender Systems
- Awesome Knowledge Distillation
- Knowledge Distillation
- Distill and transfer learning for robust multitask RL
- Geometric Deep Learning
- Visualizing and Understanding Generative Adversarial Networks
- Trust Region Policy Optimization
- Generative Adversarial Imitation Learning
- Google AI's Take on How To Fix Peer Review
- Towards Generalization and Efficiency in Reinforcement Learning
- Acceptance Rate of AI Conferences
- PyTorch Geometric
- SQL Query Optimization Meets Deep Reinforcement Learning
- Time Series Forecasting using Statistical and Machine Learning Models
- BMM Summer Course 2018 Slides
- A Style-Based Generator Architecture for GAN
- Multi-Agent Reinforcement Learning
- What Makes a Good Image Generator AI?
- Automatic Machine Learning
- Generalizing Convolutions for Deep Learning
- Unsupervised Deep Learning
- Manifold Learning Yields Insight into Complex Biological State Space
- Project Malmo – a platform for fundamental AI research
- Keras vs. PyTorch
- Variational Inference - Foundations and Innovations
- The GAN Zoo
- Beautiful Layered Materials, Instantly
- 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
- Neural Image Stitching And Morphing
- Anomaly Detection for Real-World Systems
- Anomaly Detection
- What Explains Perception in the Brain?
- Applied Time Series Econometrics in Python and R
- Time Series Analysis with Python Intermediate
- Two Effective Algorithms for Time Series Forecasting
- AI Learns Painterly Harmonization
- Deep Learning with Ensembles of Neocortical Microcircuits
- A Neural Network Model That Can Reason
- 누구나 할 수 있는 엄청난 몰입력의 5가지 단계
- This AI Reproduces Human Perception
- 미루는 버릇을 완전히 없애주는 '2분짜리 간단한 습관'
- From Generative Models to Generative Agents
- World Models
- What Can Machine Learning Do? Workforce Implications
- Introduction to AI for Video Games
- 3D Generative-Adversarial Modeling
- Generative Models
- The Design of Traffic Flow Prediction System Based on Multimodal Data
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- 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
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- How to prepare for SDN
- Features of Cloud and Grid Platforms
- 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
- Software-Defined Networks
- Future of the Network Documentary
- A Leap Forward With SQL on Hadoop
- Zero-Copy
- Remote Direct Memory Access
- Special "How it Works" Big Data & Brews
- Choosing distribution models
- GPU Programming for the Data Sciences
- 10 undeniable reasons people hate Big Data
- Physically Based Shading in Theory and Practice
- The Forgotten History Of CGI
- Hadoop Sector will Have Annual Growth of 58% for 2013-2020
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- Apache Spark Resource Management and YARN App Models
- Measures of Query Cost
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- ACID Properties of Transactions
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- Introduction to Distributed Database Management Systems
- Development Tools for Big Data
- Introduction to Data Warehousing
- Apache Tajo JDBC Driver
- Date's Twelve Rules for a DDBMS
- Apache Tajo HCatalog Integration
- Spark for Data Science; A Case Study
- Intel® Xeon Phi™ Coprocessor - the Architecture
- Apache Tajo Backup and Restore
- Apache Tajo Table Partitioning
- An Insight into Hadoop Yarn Resource Manager
- Regression Analysis
- 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
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- Apache Tajo CSV (TextFile)
- Papers to Read
- Burndown Chart
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- Apache Tajo™ 0.8.0 Released!
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- The Stinger Initiative From HortonWorks With Hadoop
- GPU Cluster
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- Chubby
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- The Google File System (GFS)
- Stinger Intiative - Making Apache Hive 100 Times Faster
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- PageRank Algorithm
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- Simple YARN Application
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- 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
- Resource Management Fundamentals
- NewSQL Vs. SQL on Hadoop
- Deep Learning
- Presto as a Service
- Apache Hadoop YARN – Related Work
- Advanced and Distributed Operating Systems
- Adding ACID to Apache Hive
- Eight SQL on Hadoop Challenges
- Computer Science @ University of Cambridge
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- Bloom Filter
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- Interactive Query for Hadoop with Apache Hive on Apache Tez
- Introducing Apache Tez 0.4
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- Failures in YARN
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- YARN Framework and Fault Tolerance
- Smart Things vs. Many Experienced Things
- Apache Hadoop YARN – Node Manager (NM)
- Hardening Hadoop for Healthcare with Project Rhino
- Apache Tajo
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- Apache Hadoop YARN – Application Master (AM)
- Apache Hadoop in 2013 - The State of the Platform
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- Design Objectives of Computer Clusters
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- Apache Hadoop YARN – Architecture Overview
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- Apache Hadoop YARN – History and Rationale, Shared Clusters
- Functional Programming
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- Implementing Graph-Based Applications
- Designing Graph-Based Applications
- Depth of Knowledge
- Data-intensive Computing
- Mapping and Folding and the Map-Reduce Paradigm
- Codd's 12 Rules
- OLAP vs. OLTP
- Network Topology and Hadoop
- Comparison - Ad-hoc vs Stored Procedure vs Dynamic SQL
- Ensuring the Validity, Veracity, and Volatility of Big Data
- Steve Jobs at SIGGRAPH 1995
- Operationalizing Big Data
- Modern Oil & Gas Architectures Built with Hadoop
- Using Avro in MapReduce Jobs with Pig
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- MapReduce and Hadoop Papers in the VLDB
- NoSQL Databases
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- Big Data Dilemma
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- The Data Science Process
- Statistical Thinking in the Age of Big Data
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- Thought Experiment - Meta-Definition
- A Data Science Profile
- The Role of the Social Scientist in Data Science
- What is Data Science? - The Current Landscape
- Community Detection - Clustering Coefficient
- What is Data Science?
- Stream Data Processing for Performance Evaluation of the Equipment in an Offshore Plant
- 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
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- Recommendations
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- Facebook Presto
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- 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
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- Papers about MapReduce
- Introducing Apache Hadoop YARN
- T-Shaped Data Scientists
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- Tez Design - Introduction
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- Continuous Query Semantics and Operators (Part II)
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- Parallel R
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- Introduction to the Tez
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- How Could Machines Reach Human-Level Intelligence?
- Bayesian Inference - Parametric Inference, Predictive Inference
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- Change of Variable Rule
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- 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
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- How Domain-Specific AI Agents Will Shape the Industrial World
- Harvard Presents NEW Knowledge-Graph AGENT (MedAI)
- ParadeDB – Postgres for Search and Analytics
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- Rethinking Physics Informed Neural Networks
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- High Level Overview of AI and ML in Science and Engineering
- A Trip Down the Generative Graphics Pipeline
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- Morris Approximate Counter
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- Language Models as World Models
- Building LLMs from the Ground Up
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- GraphRAG; The Marriage of Knowledge Graphs and RAG
- Decoding the Human Brain – A Neurosurgeon’s Experience
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- 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
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- A tutorial on on neurosymbolic techniques
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- 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
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- Meta AI, Limits of LLMs, AGI & the Future of AI
- Analyze Data with LLMs and Python
- DuckDB Spatial; Supercharged Geospatial SQL
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- 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
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- 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
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- Learn PostgreSQL fast! | PostgreSQL for beginners
- The Influence of Computation on Inference
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- On the Generalization Capability of Temporal Graph Learning Algorithms
- Spectral Graph Neural Networks
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- 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
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- Grounding for Gemini with Vertex AI Search and DIY RAG
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- LangGraph 101; it's better than LangChain
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- Uncovering and Inducing Interpretable Causal Structure
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- Prof. Chris Bishop's NEW Deep Learning Textbook!
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- Gemini Full Breakdown + AlphaCode 2 Bombshell
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- Multimodal Prompting with a 44-minute Movie
- Approximate Maximum Matching
- Prompt Engineering, RAG, and Fine-tuning
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- Entropy & Mutual Information in Machine Learning
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- The Science of LLM Benchmarks - Methods, Metrics, and Meanings
- Formal Methods and Machine Learning
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- 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
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- 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
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- 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
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- 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
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- 5 Explosive Digital Marketing Strategies
- Introduction to Vector Search
- Atlan AI for Documentation -- LIVE Demo
- LLMOps; Everything You Need to Know to Manage LLMs
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- Reset KOREA!, The 1st Seminar
- Intro to Large Language Models
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- 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
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- Generative AI And Programming
- Gartner's Top 10 Tech Trends for 2024
- Making Retrieval Augmented Generation Better
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- Tree of Thought Prompting
- Building a managed database service with Kubernetes Operators
- A Purpose-Built Vector Data Management System
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- 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
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- Embeddings, Vector Databases, and Search
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- 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
- 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
- Continuous Training and Deployment with ZenML and Seldon
- CVPR Highlights 2023
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