Public facing deeplift repo

Pytorch implementation of various neural network interpretability methods

SyReNN: Symbolic Representations for Neural Networks

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

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

Integrated gradients attribution method implemented in PyTorch

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

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

simple implementation of Expected Gradients and Integrated Gradients by pytorch

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.

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)

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.

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

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

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

Prediction and interpretable analysis of short-term precipitation using deep learning models

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

The code for integrated gradients in torch.

A lightweight Explainable AI CNN for PathMNIST medical imaging, achieving 91%+ accuracy with Integrated Gradients and SQLite-based attribution storage. Built in PyTorch, this scalable model delivers high performance, transparency, and real-world readiness, making it ideal for medical AI, edge deployment, and explainable deep learning research.

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