#Machine Learning Theory
#Statistical Learning
- Gaussian Process Regression
- Strong Convexity on Gradient Descent and Newton's Method
- Bayesian Statistics
- Yarin Gal Thesis (Uncertainty in Deep Learning) Chapter 3
- Interpreting Deep Neural Networks Through Variable Importance
- Stochastic Blockmodels meet Graph Neural Networks
- Active Learning for Community Detection in Stochastic Block Models
- Sampling methods for statistical inference
#Reinforcement Learning
#XAI
- Understanding Blackbox Prediction via Influence Functions
- WWW 2020 XAI Tutorial
- Interpreting Deep Neural Networks Through Variable Importance
- Stochastic Blockmodels meet Graph Neural Networks
#Mathematics
#Vision
- Super Resolution Intro.
- PointNet Deep Learning on Point Sets for 3D Classification and Segmentation
- Unsupervised Methods for Image Super-Resolution
- Dynamic Graph CNN for Learning on Point Clouds
- Probabilistic lane detection and lane tracking for autonomous vehicles
- Demystifying Neural Style Transfer
#NLP
- Attention Is All You Need
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Scaling Laws for Neural Language Models
#Machine Learning
- Decision Tree
- Locality Sensitive Hashing (LSH)
- Towards Deep Learning Models Resistant to Adversarial Attacks
- Probabilistic lane detection and lane tracking for autonomous vehicles
- Yarin Gal Thesis (Uncertainty in Deep Learning) Chapter 3
- Block Model Guided Unsupervised Feature Selection
- Basic models and questions in statistical network analysis (Lecture 1)
- Sampling methods for statistical inference
- Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
- Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer
#Statistics
- Elementary Statistical Inference
- Bayesian Statistics
- The Robustness of Okun's Law
- Sampling methods for statistical inference
#Machine Learning Systems
#Generative Model
- Variational Autoencoder and Generative Adversarial Networks
- Demystifying Neural Style Transfer
- Neural Topic Modeling with Continual Lifelong Learning
#DL
#Unsupervised Learning
- Unsupervised Methods for Image Super-Resolution
- Neural Topic Modeling with Continual Lifelong Learning
#Opimization
#Data Mining
#Attention Model
#Clean Code
#Python
#Differential Geometry
#Image Processing
#Fourier Transform
#Wavelet
#GNN
- Semi-supervised Learning with Graph Learning-Convolutional Networks
- The Ways of Node Embedding - Node2vec, DeepWalk
- Dynamic Graph CNN for Learning on Point Clouds
- MolGAN - An implicit generative model for small molecular graphs
- GNNs - A Review of Methods and Applications
- Fourier and Wavelet transform in GCN
- Block Model Guided Unsupervised Feature Selection
- Stochastic Blockmodels meet Graph Neural Networks
#Node embedding
#Signal Processing
#Deep Learning
- Yarin Gal Thesis (Uncertainty in Deep Learning) Chapter 3
- Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
- Reducing Domain Gap via Style-Agnostic Networks
- Semantic Image Synthesis with Spatially-Adaptive Normalization
#Datamining
#Statistiacl Learninig
#Stochastic Block Model
#Active Learning
#Bayesian Statistics
- Sampling methods for statistical inference
- Why bigger is not always better: on finite and infinite neural networks
#Multi-Task Learning
- Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
- Federated Learning intro
#Recommender System
#Meta Learning
#Topic Modeling
#Deep Learning Theory
- Why bigger is not always better: on finite and infinite neural networks
- Scaling Laws for Neural Language Models
- Learning Curve Theory
#Federated Learning
#Massive AI
- Scaling Laws for Neural Language Models
- Neural Topic Modeling with Continual Lifelong Learning
- Learning Curve Theory
#Sports Analysis