kundajelab/deeplift

Public facing deeplift repo

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hans66hsu/nn_interpretability

Pytorch implementation of various neural network interpretability methods

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95616ARG/SyReNN

SyReNN: Symbolic Representations for Neural Networks

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TooTouch/WhiteBox-Part1

In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

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robinvanschaik/interpret-flair

A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.

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hummat/saliency

PyTorch implementation of 'Vanilla' Gradient, Grad-CAM, Guided backprop, Integrated Gradients and their SmoothGrad variants.

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DFKI-NLP/SMV

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)

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Sanofi-Public/Clinical-BERT-Explainability

To explain clinical BERT model predictions, we present an approach which leverages integrated gradients to attribute events in medical records that lead to an outcome prediction.

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hobinkwak/ExpectedGradients_IntegratedGradients_pytorch

simple implementation of Expected Gradients and Integrated Gradients by pytorch

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CVxTz/IntegratedGradientsPytorch

Integrated gradients attribution method implemented in PyTorch

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daniel-rychlewski/Cao-Keras-Visualization

Neural network visualization tool after an optional model compression with parameter pruning: (integrated) gradients, guided/visual backpropagation, activation maps for the cao model on the IndianPines dataset

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ChristianOrr/feature-attribution-methods-jax

Suite of methods that create attribution maps from image classification models.

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riverback/pytorch_attribution

Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.

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Featurespace/uncertainty-attribution

Scripts to reproduce results within the following manuscript: Perez, I., Skalski, P., Barns-Graham, A., Wong, J. and Sutton, D. (2022) Attribution of Predictive Uncertainties in Classification Models, 38th Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022.

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zxlzr/DiagnoseAdv

Source code for the IJCKG2021 paper "Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction".

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KurtButler/2024_attributions_paper

Reproducible code for our paper "Explainable Learning with Gaussian Processes"

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Machine-Learning-Foundations/day_14_exercise_interpretability

Exercise on interpretability with integrated gradients.

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allenwind/text-integrated-gradients

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aancyl/Cirrhosis-Fatality-Prediction

Accurately predicting patient outcomes is crucial for clinical decision-making and resource planning. In this project, we utilize patient data to build a predictive model that determines the survival status based on various clinical features.

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NajdBinrabah/Deep-Learning-with-PyTorch-and-Captum

This project classifies smoking images using VGG19 with data augmentation, and Captum for model explainability, identifying key features per prediction.

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meetpatel0963/TeleXGI

TeleXGI: Explainable Gastrointestinal Image Classification for TeleSurgery Systems

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pjlintw/integrated-gradients

Implementation for Conditional Text GANs and Analysis with Integrated Gradients

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Engrima18/ExaplainNET

Implementing algorithms based on the analysis of the gradients in NN computational graphs to provide nice insights for Explainable AI

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adlyZaroui/XAI

Comparative Analysis of XAI Methods for Medical Diagnostic using Chest X-ray Images

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sherlcok314159/Integrated-Gradients

The code for integrated gradients in torch.

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Siddhantmest/XAI-Methods-for-CNN

Implementation of 2 XAI methods to visualize the region highlighted by the network to make a prediction

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