Implementing algorithms based on the analysis of the gradients in NN computational graphs to provide nice insights for Explainable AI
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Updated
Dec 6, 2023 - Jupyter Notebook
Implementing algorithms based on the analysis of the gradients in NN computational graphs to provide nice insights for Explainable AI
Implementation for Conditional Text GANs and Analysis with Integrated Gradients
Comparative Analysis of XAI Methods for Medical Diagnostic using Chest X-ray Images
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
Exercise on interpretability with integrated gradients.
Source code for the IJCKG2021 paper "Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction".
Implementation of 2 XAI methods to visualize the region highlighted by the network to make a prediction
The code for integrated gradients in torch.
Suite of methods that create attribution maps from image classification models.
Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.
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.
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)
simple implementation of Expected Gradients and Integrated Gradients by pytorch
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
PyTorch implementation of 'Vanilla' Gradient, Grad-CAM, Guided backprop, Integrated Gradients and their SmoothGrad variants.
Integrated gradients attribution method implemented in PyTorch
A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.
SyReNN: Symbolic Representations for Neural Networks
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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