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
Neural network visualization toolkit for tf.keras
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
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.
Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
GraphXAI: Resource to support the development and evaluation of GNN explainers
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
🗺️ Data Cleaning and Textual Data Visualization 🗺️
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
Amazon SageMaker Solution for explaining credit decisions.
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"
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
STAGIN: Spatio-Temporal Attention Graph Isomorphism Network
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