Public facing deeplift repo
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Updated
Apr 28, 2022 - Python
Public facing deeplift repo
Pytorch implementation of various neural network interpretability methods
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
SyReNN: Symbolic Representations for Neural Networks
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
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
A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.
Exercise on interpretability with integrated gradients.
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)
Attribution methods that explain image classification models, implemented in PyTorch, and support batch input and GPU.
Implementation of 2 XAI methods to visualize the region highlighted by the network to make a prediction
Source code for the IJCKG2021 paper "Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction".
Suite of methods that create attribution maps from image classification models.
The code for integrated gradients in torch.
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.
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
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