A curated list of awesome contrastive explanation in ML resources
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Updated
Mar 18, 2020
A curated list of awesome contrastive explanation in ML resources
A Python package for biomarkers identification powered by interpretable deep learning
Pseudo-label supervised graph neural network for robust, fine-grained, interpretable spatial domain identification.
Tutorial on Representer Point Selection for Explaining Deep Neural Networks (CIFAR-10)
Official Implementation of TMLR's paper: "TabCBM: Concept-based Interpretable Neural Networks for Tabular Data"
Master Thesis on reproducibility and interpretability of neural ranking models
Code for Surgical Skill Assessment via Video Semantic Aggregation (MICCAI 2022)
Pytorch example of path-explain using Pytorch
Working Memory Inspired Hierarchical Video Decomposition with Transformative Representations
neural network to learn paths in decision tree
Facial emotion classification and modification using CNNs.
BRACE - BetteR Accuracy from Concept-based Explanation
Undergraduate thesis of Post-hoc Interpretable Deep Learning for birds sound
Paper and resources collections about interpretable AI (XAI)
Replicated “Understanding Individual Neuron Importance Using Information Theory” paper. Information Theory and Learning Course Project.
Code for my thesis about SHAP. Implementation of DecisionTree, SVM, BERT on 2 Datasets Imdb and Argument Mining
Interpretability of deep representation learning models
Awesome papers on Interpretable Machine Learning
An unofficial version of the PyTorch implementation of CURE and Fast Adversarial training with FGSM.
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