Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)

A curated list of resources on implicit neural representations.

Python package for causal discovery based on LiNGAM.

Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Python Implementation of Reinforcement Learning: An Introduction

pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.

Foundational library for Kernel methods in pattern analysis and machine learning

python code for kernel methods

Technical Analysis Library using Pandas and Numpy

Methods and Implements of Deep Clustering

PyTorch implementation of consistency regularization methods for semi-supervised learning

Battery Historian is a tool to analyze battery consumers using Android "bugreport" files.

This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).

BERT-related papers

人像卡通化探索项目 (photo-to-cartoon translation project)

Vim plugin for intensely nerdy commenting powers

This repository contains a pytorch implementation for the paper: Multi-Level Variational Autoencoder (https://arxiv.org/abs/1705.08841), which was accepted at AAAI-18.

PyTorch implementation of Deep Ordinal Regression Network for Monocular Depth Estimation

TikZ library for drawing Bayesian networks, graphical models and (directed) factor graphs in LaTeX.

A curated list of resources for Learning with Noisy Labels

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Advers…

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

A Keras implementation of the DeepMIML Network for multi-instance multi-label learning

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)