Official repository of our work "Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning" accepted at CVPR 2024
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Updated
Apr 8, 2024 - Python
Official repository of our work "Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning" accepted at CVPR 2024
Code and Datasets for the paper "Domain Knowledge Guided Deep Learning with Electronic Health Records", published on ICDM 2019.
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)
Pytorch example of path-explain using Pytorch
Working Memory Inspired Hierarchical Video Decomposition with Transformative Representations
Master Thesis on reproducibility and interpretability of neural ranking models
BRACE - BetteR Accuracy from Concept-based Explanation
One of the top solutions for The 2019 DII National Data Science Challenge: https://sbmi.uth.edu/dii-challenge/. More details in the paper "An interpretable deep-learning model for early prediction of sepsis in the emergency department", published on Patterns 2021.
An unofficial version of the PyTorch implementation of CURE and Fast Adversarial training with FGSM.
Personal collection of resources to get started on Interpretability in AI (... still being updated ...)
ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
Interpretability of Netflix Recommender System using Neural Networks
Official Implementation of ARACHNET: INTERPRETABLE SUB-ARACHNOID SPACE SEGMENTATION USING AN ADDITIVE CONVOLUTIONAL NEURAL NETWORK
Explainable AI for Image Classification
Facial emotion classification and modification using CNNs.
Undergraduate thesis of Post-hoc Interpretable Deep Learning for birds sound
Replicated “Understanding Individual Neuron Importance Using Information Theory” paper. Information Theory and Learning Course Project.
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