arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

Implemented a stable diffusion architecture using PyTorch.

A latent text-to-image diffusion model

Model interpretability and understanding for PyTorch

Implementation of the ICML 2024 paper "Discovering Mixtures of Structural Causal Models from Time Series Data"

🎓 无需编写任何代码即可轻松创建漂亮的学术网站 Easily create a beautiful academic résumé or educational website using Hugo and GitHub. No code.

Granger causality discovery for neural networks.

Causal discovery algorithms and tools for implementing new ones

Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at

A list of companies of possible interest for mathematicians (or related) that are looking for a job in quantitative finance in Zurich.

A simple feature-based time series classifier using Kolmogorov–Arnold Networks

A beautiful, simple, clean, and responsive Jekyll theme for academics

Fast Double Precision Matrix Matrix Multiply

A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP

A reading list for papers on causality for natural language processing (NLP)

This repo contains material for the paper "Returning the Favour: When Regression Benefits from Probabilistic Causal Knowledge" ICML 2023 paper

Official Repository for the Uni-Mol Series Methods

An implementation of 1D, 2D, and 3D positional encoding in Pytorch and TensorFlow

Paper implementations from scratch and machine learning tutorials

A resource for learning about Machine learning & Deep Learning

Repository for few-shot learning machine learning projects

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

A Collection of Variational Autoencoders (VAE) in PyTorch.

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

[CVPR 2019]: Pluralistic Image Completion

Learning in infinite dimension with neural operators.

Compute credible intervals quickly