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PyTorch implementation of "Neural Optimal Transport" (ICLR 2023 Spotlight)
PyTorch implementation of Variational Diffusion Models.
Unofficial implementation of Variational Diffusion Models in PyTorch (Lightning)
Scalable Gaussian Process Regression Networks
Code for *ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models*
Sequence modeling benchmarks and temporal convolutional networks
Structured state space sequence models
PyTorch Implementation of Diffusion Schrodinger Bridge Matching
The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021)
Reinforcement Learning based 'Learning' of Dynamic Sepsis Treatment Strategies
Stable Diffusion web UI
v objective diffusion inference code for PyTorch.
Cracking the Coding Interview 6th Ed. Python Solutions
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Freddie Mac Single Loan Data Analysis & Machine Learning (Regression / Classification)
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
Offline Risk-Averse Actor-Critic (O-RAAC). A model-free RL algorithm for risk-averse RL in a fully offline setting
Deep Reinforcement Learning for Portfolio Optimization
Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
Code for "Latent ODEs for Irregularly-Sampled Time Series" paper
Learning representations for RL in Healthcare under a POMDP assumption
Toolbox of models, callbacks, and datasets for AI/ML researchers.
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gan…
A collection of reference environments for offline reinforcement learning