Deep Insight And Neural Network Analysis
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Updated
May 31, 2024 - Jupyter Notebook
Deep Insight And Neural Network Analysis
🤖 Making AI understandable and transparent, enhancing trust and accountability.
Local interpretability for survival models
Data science projects at Aboitiz
moDel Agnostic Language for Exploration and eXplanation
This repo contains the code of my Master's Thesis. Specifically, it consists in exploring different techniques(Explanable AI, Physics Informed NN, ...) to perform State Estimation
Local Universal Rule-based Explanations
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Awesome Heart Sound Analysis - A Survey
Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.
Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images
Counterfactuals: Take the uncertainty out of your machine learning models
Explainable Artificial Intelligence through Contextual Importance and Utility
Endocrine Disruption Explainer is a code to generate structural alerts of endocrine disruption of chemcial compounds using Local Interpretable Model-Agnostic Explanations (LIME) of machine learning models from TOX-21, EDC, and EDKB-FDA datasets.
This project provides GOLang implementation of Neuro-Evolution of Augmenting Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima
Official Implementation of TMLR's paper: "TabCBM: Concept-based Interpretable Neural Networks for Tabular Data"
An Open-Source Library for the interpretability of time series classifiers
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.
ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
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