XAI - An eXplainability toolbox for machine learning
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Updated
Oct 30, 2021 - Python
XAI - An eXplainability toolbox for machine learning
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
Official implementation of Score-CAM in PyTorch
Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Neural network visualization toolkit for tf.keras
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
For calculating global feature importance using Shapley values.
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
Amazon SageMaker Solution for explaining credit decisions.
GraphXAI: Resource to support the development and evaluation of GNN explainers
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
The implementation of “A Capsule Network for Recommendation and Explaining What You Like and Dislike”, Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu, https://dl.acm.org/citation.cfm?doid=3331184.3331216
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
STAGIN: Spatio-Temporal Attention Graph Isomorphism Network
Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
For calculating Shapley values via linear regression.
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