99 Open source projects that are alternatives of or similar to ConceptBottleneck

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

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

Fit interpretable models. Explain blackbox machine learning.

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

A curated list of awesome machine learning interpretability resources.

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.

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

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

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

StellarGraph - Machine Learning on Graphs

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

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

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

[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.

📍 Interactive Studio for Explanatory Model Analysis

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

A Tensorflow retrieval (space embedding) baseline. Metric learning baseline on CUB and Stanford Online Products.

Pytorch Implementation of recent visual attribution methods for model interpretability

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

A collection of full-stack resources for programmers.

Model Agnostics breakDown plots

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

A simple app which shows the lastest movies trailers based on different genres developed using SwiftUI.

A collection of tools for reverse engineering neural networks.

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

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

Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems

Model interpretability and understanding for PyTorch

Global-Local Capsule Network (GLCapsNet) is a capsule-based architecture able to provide context-based eye fixation prediction for several autonomous driving scenarios, while offering interpretability both globally and locally.

A collection of research materials on explainable AI/ML

NeurIPS 2018. Linear-time model comparison tests.

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Concepts and ideas for the GNOME desktop

💡 A curated list of adversarial attacks on model explanations

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

Collection of NLP model explanations and accompanying analysis tools

Layer-wise Relevance Propagation (LRP) for LSTMs

Modern port of Melanie Mitchell's and Douglas Hofstadter's Copycat

A curated list of awesome Fairness in AI resources

📜 33 JavaScript concepts every developer should know.

⬛ Python Individual Conditional Expectation Plot Toolbox

Content on tips, tricks, advice, practices for naming things in in software/technology

Awesome Resources for Advanced Computer Vision Topics

The curation repository for the data behind Concepticon.

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

Data generator for Arena - interactive XAI dashboard

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

(Concepts-enabled) Functional Abstraction Layer for C++

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

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

Text classification models. Used a submodule for other projects.

Thread pool using std::* primitives from C++17, with optional priority queue/greenthreading for POSIX.

General Interpretability Package

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

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

📊 All of courses, assignments, exercises, mini-projects and books that I've done so far in the process of learning by myself Machine Learning and Data Science.

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

Molecular Explanation Generator