🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

A resource for learning about Machine learning & Deep Learning

A Collection of Variational Autoencoders (VAE) in PyTorch.

Sequence modeling benchmarks and temporal convolutional networks

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

A library for scientific machine learning and physics-informed learning

Learning in infinite dimension with neural operators.

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Code Repository for Liquid Time-Constant Networks (LTCs)

proof of concept for a transformer-based time series prediction model

Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"

Python Dynamic Mode Decomposition

Closed-form Continuous-time Neural Networks

tfts: Time Series Deep Learning Models in TensorFlow

Experiments for understanding disentanglement in VAE latent representations

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Pytorch implementation of β-VAE

Wasserstein Auto-Encoders

Physics-Informed Neural networks for Advanced modeling

pyMOR - Model Order Reduction with Python

Disentanglement library for PyTorch

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

Python package containing the tools necessary for radial basis function (RBF) applications

Adaptive FNO transformer - official Pytorch implementation

Official implementation of Dynamical VAEs

DeepONet & FNO (with practical extensions)

Boundary Equibilibrium Generative Adversarial Networks Implementation in Tensorflow