100 Open source projects that are alternatives of or similar to ArenaR

Fit interpretable models. Explain blackbox machine learning.

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

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

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

A curated list of awesome machine learning interpretability resources.

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

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

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

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

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

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

Collection of NLP model explanations and accompanying analysis tools

Papers about explainability of GNNs

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

[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.

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

For calculating global feature importance using Shapley values.

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

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

Visualization tool for Graph Neural Networks

Model Agnostics breakDown plots

Pytorch/Python3 implementation of DeepAccNet, protein model accuracy evaluator.

Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

A collection of tools for reverse engineering neural networks.

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

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

⬛ Python Individual Conditional Expectation Plot Toolbox

The collections of simple, weighted, exponential, smoothed moving averages.

Awesome Resources for Advanced Computer Vision Topics

NeurIPS 2018. Linear-time model comparison tests.

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM)

Model interpretability and understanding for PyTorch

Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.

💡 A curated list of adversarial attacks on model explanations

Text classification models. Used a submodule for other projects.

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

General Interpretability Package

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

A collection of research materials on explainable AI/ML

Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.

Algorithms for monitoring and explaining machine learning models

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

[ICCV 2017] Torch code for Grad-CAM

moDel Agnostic Language for Exploration and eXplanation

A Python package implementing a new machine learning model for text classification with visualization tools for Explainable AI

Interpretability Methods for tf.keras models with Tensorflow 2.x

Machine Learning in one line of code

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Federated Learning Library: https://fedml.ai

XAI - An eXplainability toolbox for machine learning

Visualization toolkit for neural networks in PyTorch! Demo -->

Interesting resources related to XAI (Explainable Artificial Intelligence)

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

📍 Interactive Studio for Explanatory Model Analysis

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.