XMLX GitHub configuration
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Updated
Nov 23, 2021
XMLX GitHub configuration
El proyecto se centra en la destilación de conocimiento y técnicas de explicabilidad para mejorar el rendimiento de redes neuronales en imágenes naturales.
One of the firsts dataset level explanability libraries for 1d signal using GRAD-CAM++
IREX iteratively refines tabular datasets, such as self-report questionnaires, while providing an explanation of a given classification model.
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.
Graduate research project in computer vision and deep learning explainability
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.
🤖 Making AI understandable and transparent, enhancing trust and accountability.
Strategies to interpret Deep Learning & Machine Learning models/black box; help us to understand how it’s making predictions/decisions.
Choregraphe App for Pepper robots to enable them to tell a scripted story specified in a Google spreadsheet.
Optimizing Mind static website v1
Tensorflow implementation of Deep Q-Network (DQN) and Behavior Cloning (BC) to learn how to defeat humans in a FlappyBird game.
ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)
Gastrointestinal disease classification using Contrastive and Cost-sensitive Learning
Explainable Feature Construction (EFC)
This repository contains code, information and datasets for the project on making interpretable models titled "Model Agnostic Methods for Interpretable Machine Learning". The abstract can be accessed at https://docs.google.com/document/d/1k2-beHD4YQxXpH8ExUM2Gd-yE5VqdluhiCsUIO3czRM/edit?usp=sharing
Improve Zorro exlanations for graph neural networks.
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