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aaai2022

ae-wavenet

Wavenet Autoencoder for Unsupervised speech representation learning (after Chorowski, Jan 2019)

aiops-handbook

Collection of slides, repositories, papers about AIOps

aix360

Interpretability and explainability of data and machine learning models

awesome-causality-algorithms

An index of algorithms for learning causality with data

awesome-causality-data

A data index for learning causality.

awesome-chinese-nlp

A curated list of resources for Chinese NLP 中文自然语言处理相关资料

awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

awesome-public-datasets

A topic-centric list of HQ open datasets. PR ☛☛☛

benchmarking_tsdiscretizations

Comparison & Assessment of Time Series Data Discretization on Inference Methods

causaldiscoverytoolbox

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

causality

Tools for causal analysis

causalnex

A Python library that helps data scientists to infer causation rather than observing correlation.

causing

Causing: CAUsal INterpretation using Graphs

ccit

Classifier Conditional Independence Test: A CI test that uses a binary classifier (XGBoost) for CI testing

cgnn

Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"

chinesenlp

Datasets, SOTA results of every fields of Chinese NLP

clp

COIN-OR Linear Programming Solver

crnn.pytorch

Convolutional recurrent network in pytorch

cuml

cuML - RAPIDS Machine Learning Library

cvxopt

CVXOPT -- Python Software for Convex Optimization

dag-gnn

darkskylib

Python wrapper for the Dark Sky API

dcrnn

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

dcrnn-1

PyTorch implementation of Diffusion Convolutional Recurrent Neural Network - Under development

dgl

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

dilate

Code for our NeurIPS 2019 paper "Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models"

disentangling-vae

Experiments for understanding disentanglement in VAE latent representations

disp_functions

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.