Model interpretability and understanding for PyTorch
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Updated
May 31, 2024 - Python
Model interpretability and understanding for PyTorch
Materials for "Quantifying the Plausibility of Context Reliance in Neural Machine Translation" at ICLR'24 🐑 🐑
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
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
Codes for the paper On marginal feature attributions of tree-based models
Explainable AI in Julia.
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)
Counterfactual SHAP: a framework for counterfactual feature importance
Collection of NLP model explanations and accompanying analysis tools
Feature Attribution methods for neurons and Evolution experiments
Materials for the Lab "Explaining Neural Language Models from Internal Representations to Model Predictions" at AILC LCL 2023 🔍
The official repo for the EACL 2023 paper "Quantifying Context Mixing in Transformers"
⛈️ Code for the paper "End-to-End Prediction of Lightning Events from Geostationary Satellite Images"
Bachelor's thesis for degree in Economics at HSE University, Saint-Petersburg (2022)
A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
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