🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

🦜🔗 The platform for reliable agents.

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Deep universal probabilistic programming with Python and PyTorch

Minimal keyword extraction with BERT

Code for "Heterogeneous Graph Transformer" (WWW'20), which is based on pytorch_geometric

Collection of probabilistic models and inference algorithms

Solutions of assignments of Deep Reinforcement Learning course presented by the University of California, Berkeley (CS285) in Pytorch framework

🌀 Stanford CS 228 - Probabilistic Graphical Models

Pytorch starter code for UC Berkeley's cs285 assignments

Multi-agent reinforcement learning programs based on Game theory

Berkeley CS285 2019 homework solution

Code for my ICML 2019 paper "Correlated Variational Auto-Encoders"

Deconvolutional Latent-Variable Model for Text Sequence Matching