79 Open source projects that are alternatives of or similar to Alibi

Molecular Explanation Generator

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

[ECCV 2018] code for Choose Your Neuron: Incorporating Domain Knowledge Through Neuron Importance

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

A collection of research materials on explainable AI/ML

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

Data generator for Arena - interactive XAI dashboard

Code for the TCAV ML interpretability project

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

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

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

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

Human-explainable AI.

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

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

[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.

Towards Understanding Deep Learning Representations via Interactive Experimentation

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

Federated Learning Library: https://fedml.ai

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

For calculating global feature importance using Shapley values.

Model interpretability and understanding for PyTorch

H2O.ai Machine Learning Interpretability Resources

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

official implementation of "Visualization of Convolutional Neural Networks for Monocular Depth Estimation"

Code for "Free-Lunch Saliency via Attention in Atari Agents"

📍 Interactive Studio for Explanatory Model Analysis

Fit interpretable models. Explain blackbox machine learning.

Concept Bottleneck Models, ICML 2020

Interesting resources related to XAI (Explainable Artificial Intelligence)

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

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

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

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.

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.

Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick T., Hovy E. AAAI 2018

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

Public facing deeplift repo

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

A library for finding knowledge neurons in pretrained transformer models.

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

moDel Agnostic Language for Exploration and eXplanation

NeurIPS 2018. Linear-time model comparison tests.

🏔️ Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

Collection of NLP model explanations and accompanying analysis tools

A collection of infrastructure and tools for research in neural network interpretability.

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

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

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

XAI - An eXplainability toolbox for machine learning

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

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

A curated list of awesome machine learning interpretability resources.

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

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 2017] Torch code for Grad-CAM

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

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

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

Python package to visualize and cluster partial dependence.