MICCAI 2022 (Oral): Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
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Updated
Apr 29, 2023 - Python
MICCAI 2022 (Oral): Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
[KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"
(ICML 2023) Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Official code for the CVPR 2022 (oral) paper "OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks."
[ICCV 2023] Learning Support and Trivial Prototypes for Interpretable Image Classification
[TPAMI 2025] Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition
TraceFL is a novel mechanism for Federated Learning that achieves interpretability by tracking neuron provenance. It identifies clients responsible for global model predictions, achieving 99% accuracy across diverse datasets (e.g., medical imaging) and neural networks (e.g., GPT).
hopwise: A Python Library for Explainable Recommendation based on Path Reasoning over Knowledge Graphs
Semi-supervised Concept Bottleneck Models (SSCBM)
Explainable Speaker Recognition
Visualization methods to interpret CNNs and Vision Transformers, trained in a supervised or self-supervised way. The methods are based on CAM or on the attention mechanism of Transformers. The results are evaluated qualitatively and quantitatively.
Counterfactual explanations for continuous reinforcement learning with simulation in T1D and Gym environments.
Implementation of the gradient-based t-SNE sttribution method described in our GLBIO oral presentation: 'Towards Computing Attributions for Dimensionality Reduction Techniques'
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