Wine quality multi-class prediction neural net model implemented using pytorch with model exploration and explanation using shap.
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Updated
Mar 10, 2020 - Jupyter Notebook
Wine quality multi-class prediction neural net model implemented using pytorch with model exploration and explanation using shap.
Public repository for "A deep learning toolkit for visualization and interpretation of segmented medical images"
Multiscale Optimization Analysis of COVID-19 Spatiotemporal Variations
Paper and resources collections about interpretable AI (XAI)
This repo holds my attempt to explain fake news detection models.
Kaggle Machine Learning Courses Exercises
Slot Attention-based Classifier for Explainable Image Recognition
Comparison of sentiment analysis conducted with a lexicon and rule-based dictionary and state-of-the-art pre-trained language models
Code for paper "XPROAX - Local explanations for text classification with progressive neighborhood approximation", DSAA 2021 (https://ieeexplore.ieee.org/abstract/document/9564153). Repository maintained by Yi Cai.
Fundamentals of Interpretable Data Science
GitHub repository for our work "Interpretable Machine Learning for Precision Aging"
In this repository you will fine explainability of machine learning models.
Code for the paper "Personalized Algorithmic Recourse with Preference Elicitation"
Visualize the low-level outputs of YOLOv8 to analyze and understand the areas where our model focuses. Specifically, illustrate which anchor points are activated to predict bounding boxes.
Area Over Perturbation Curve using Most Relevant Feature for semantically evaluate XAI methods
LIME for TimeSeries enhances AI transparency by providing LIME-based interpretability tools for time series models. It offers insights into model predictions, fostering trust and understanding in complex AI systems.
📈 [CHI 2023] Results of the statistical analysis applied to the UTA11 guide.
Code for my thesis about SHAP. Implementation of DecisionTree, SVM, BERT on 2 Datasets Imdb and Argument Mining
Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet
Benchmark to Evaluate EXplainable AI
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