Conway's Game of Life is sequential, here high-dimensional states are projected into the two-dimensional space, and connected, furthermore, meta-data is added to create interactive 2D visualizations.

This module extends the kernel SHAP method (as introduced by Lundberg and Lee (2017)) which is local in nature, to a method that computes global SHAP values.

A curated list of awesome contrastive explanation in ML resources

Evaluation framework for post hoc explanation methods | Explainable AI (XAI)

A visual debugging tool for fine-tuned BERT models for (multilabel) sequence classification tasks

To provide better version control between my contributions and my lab mate's contributions.

Explainable Deep Neural Networks for MRI Based Stroke Analysis

Interpreting Categorical Data Classifiers using Explanation-based Locality

Graduate research project in computer vision and deep learning explainability

Source code of NeurIPS'21 paper: Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach

GENIA: Study of gender biases in machine learning models using explainable artificial intelligence

Human vs AI: Frontend📱

Slides of my talk "Is Your ML Model Trustworthy?" at the MLOps World Conference on the 16th of June 2021.

Interpretable Transformer for Fine-grained Image Classification

Official implementation of "A matter of attitude: Focusing on positive and active gradients to boost saliency maps"

[CVPR 2024] CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation

This is a list of awesome prototype-based papers for explainable artificial intelligence.

CAM, Grad-CAM, Grad-CAM++ and Guided Backpropagation post-hoc explanation methods

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.