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Extensive tutorials for learning how to build deep learning models for causal inference using selection on observables in Tensorflow 2.

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

Must-read papers and resources related to causal inference and machine (deep) learning

A curated list of Visual Question Answering(VQA)(Image/Video Question Answering),Visual Question Generation ,Visual Dialog ,Visual Commonsense Reasoning and related area.

Re-implementation for paper 'Semi-supervised learning with deep generative models' using lasagne. There is no guarantee for bug free.

A curated list of awesome deep causal learning methods

Resources to learn more about Machine Learning and Artificial Intelligence

Must-read Papers on Textual Adversarial Attack and Defense

An adversarial example library for constructing attacks, building defenses, and benchmarking both

This repo keeps track of popular provable training and verification approaches towards robust neural networks, including leaderboards on popular datasets and paper categorization.

Reading list for research topics in multimodal machine learning

Sandbox for training deep learning networks

95.47% on CIFAR10 with PyTorch

Visual Question Answering Paper List.

A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.

A pytorch adversarial library for attack and defense methods on images and graphs

🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

PyTorch implementations of Adversarial attacks and utils.

Related papers for robust machine learning

[NeurIPS 2020] "Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free" by Haotao Wang*, Tianlong Chen*, Shupeng Gui, Ting-Kuei Hu, Ji Liu, and Zhangyang Wang

A lightweight experimental logging library

500 AI Machine learning Deep learning Computer vision NLP Projects with code

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

This repository captures source code and data sets for our paper at the Causal Discovery & Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems (NeurIPS) 2020.

Domain Adaptation as a Problem of Inference on Graphical Models

The implementation of the SHOT-VAE model in paper "SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations"