305 Open source projects that are alternatives of or similar to concept-based-xai

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Fit interpretable models. Explain blackbox machine learning.

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Data generator for Arena - interactive XAI dashboard

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Papers about explainability of GNNs

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

Molecular Explanation Generator

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Visualization tool for Graph Neural Networks

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

MindsDB server allows you to consume and expose MindsDB workflows, through http.

For calculating global feature importance using Shapley values.

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Collection of NLP model explanations and accompanying analysis tools

A lightweight implementation of removal-based explanations for ML models.

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Dataset for paper "Weak Supervision for Fake News Detection via Reinforcement Learning" published in AAAI'2020.

Self-training with Weak Supervision (NAACL 2021)

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Weakly supervised medical named entity classification

Fast approximate Shapley values in R

Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch

Weakly Supervised End-to-End Learning (NeurIPS 2021)

Implementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

A game theoretic approach to explain the output of any machine learning model.

Code for the paper "DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors".

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

A curated list of awesome machine learning interpretability resources.

WRENCH: Weak supeRvision bENCHmark

Automatically labeling training data

EfficientMORL (ICML'21)

Keras implementations of three language models: character-level RNN, word-level RNN and Sentence VAE (Bowman, Vilnis et al 2016).

A curated (most recent) list of resources for Learning with Noisy Labels

Code for ECCV 2020 paper (oral): Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

[ICCVW 2021] Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement

"TreeView - sub-cells simplified" (c). Enable subcells in UITableView with a single drop-in extension. CocoaPod:

Python library for Ceteris Paribus Plots (What-if plots)

Keras implementation of a Variational Auto Encoder with a Concrete Latent Distribution

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

A cheat sheet for finding similar concepts and operators in different functional languages

[CVPR 2021] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

NeurIPS 2018. Linear-time model comparison tests.

No description or website provided.

A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in rs-fMRI Data

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.

The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.

A system for quickly generating training data with weak supervision