Model interpretability and understanding for PyTorch
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Updated
Sep 9, 2024 - Python
Model interpretability and understanding for PyTorch
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
An Open-Source Library for the interpretability of time series classifiers
The official repo for the EACL 2023 paper "Quantifying Context Mixing in Transformers"
Code and data for the ACL 2023 NLReasoning Workshop paper "Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods" (Feldhus et al., 2023)
⛈️ Code for the paper "End-to-End Prediction of Lightning Events from Geostationary Satellite Images"
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
Efficient and Accurate Explanation Estimation with Distribution Compression (ICML 2024 Workshops)
Feature Attribution methods for neurons and Evolution experiments
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