Efficient and Accurate Explanation Estimation with Distribution Compression (ICML 2024 Workshops)
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Updated
Jun 27, 2024 - Python
Efficient and Accurate Explanation Estimation with Distribution Compression (ICML 2024 Workshops)
Understanding the UNet for Traffic Forecasting Task - A Visual Analytics Approach
Feature Attribution methods for neurons and Evolution experiments
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
Bachelor's thesis for degree in Economics at HSE University, Saint-Petersburg (2022)
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
Codes for the paper On marginal feature attributions of tree-based models
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
⛈️ Code for the paper "End-to-End Prediction of Lightning Events from Geostationary Satellite Images"
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)
The official repo for the EACL 2023 paper "Quantifying Context Mixing in Transformers"
Materials for "Quantifying the Plausibility of Context Reliance in Neural Machine Translation" at ICLR'24 🐑 🐑
Materials for the Lab "Explaining Neural Language Models from Internal Representations to Model Predictions" at AILC LCL 2023 🔍
Counterfactual SHAP: a framework for counterfactual feature importance
A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.
Explainable AI in Julia.
An Open-Source Library for the interpretability of time series classifiers
Collection of NLP model explanations and accompanying analysis tools
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Model interpretability and understanding for PyTorch
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