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Search results for machine learning explainable ai
explainable-ai
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machine-learning
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133 search results found
Recourse
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12
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Mllp
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12
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Hc_ml
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12
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Vbridge
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12
Visualization for Explainable Healthcare Models
Hroch
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11
Extremly fast c++/python symbolic regression library based on parallel local search.
Cnn_explainer
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11
Making CNNs interpretable.
Compare Xai
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10
A Unified Approach to Evaluate and Compare Explainable AI methods
Pytolemaic
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10
Toolbox for analysis of model's quality and model's description. For further details see
Survey Attention Medical Imaging
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10
Implementation of the paper "A survey on attention mechanisms for medical applications: are we moving towards better algorithms?" by Tiago Gonçalves, Isabel Rio-Torto, Luís F. Teixeira and Jaime S. Cardoso.
Label Free Xai
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10
This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.
Article Information 2019
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10
Article for Special Edition of Information: Machine Learning with Python
Slisemap
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9
SLISEMAP: Combining supervised dimensionality reduction with local explanations
Quantified Sleep
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9
Quantified Sleep: Machine learning techniques for observational n-of-1 studies.
Responsible Ai Workshop
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9
Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice
Transparency Guidelines
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8
Minimize the risks and maximize the benefits of using data-driven technologies within government processes, programs and services through transparency. | Réduire les risques et à maximiser les avantages liés à l’utilisation de technologies axées sur les données, dans le cadre de processus, programmes et services gouvernementaux, grâce à la transparence.
Atgfe
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8
Automated Transparent Genetic Feature Engineering
Iai Clinical Decision Rule
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8
Interpretable clinical decision rules for predicting intra-abdominal injury.
U Cam
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8
Visual Explanation using Uncertainty based Class Activation Maps
Explabox
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8
Explore/examine/explain/expose your model with the explabox!
Anchorsonr
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8
Implementation of the Anchors algorithm: Explain black-box ML models
Xai Analytics
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7
XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.
Localice
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7
Local Individual Conditional Expectation (localICE) is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI)
Counterfactual Explanations Mdp
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7
Code for "Counterfactual Explanations in Sequential Decision Making Under Uncertainty", NeurIPS 2021
Pygol
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7
A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.
Invert
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6
Official GitHub for the paper "Labeling Neural Representations with Inverse Recognition"
Tinyshap
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6
Python package providing a minimal implementation of the SHAP algorithm using the Kernel method
Deepcoda
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6
Deep learning for personalized interpretability for compositional health data
Explaining Deep Clinical Gait Classification
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6
Code and Data used for the paper "Explaining Machine Learning Models for Clinical Gait Analysis"
Shapley_valuation
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6
PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]
Keras Explainable
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5
Efficient explaining AI algorithms for Keras models
Calimocho
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5
Explanatory Interactive Machine Learning with Self-explaining Neural Networks
Deep Learning Resources
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5
Curated list of DL Resources [Updated 2019]
Xaisuite
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5
XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.
Trustyai Explainability Python Examples
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5
Examples for the Python bindings for TrustyAI's explainability library
Pyslise
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5
Robust regression algorithm that can be used for explaining black box models (Python implementation)
Slise
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5
Robust regression algorithm that can be used for explaining black box models (R implementation)
Xi Method
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5
Xi method
Cf Shap
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5
Counterfactual SHAP: a framework for counterfactual feature importance
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