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73 search results found
Pytorch Grad Cam
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8,723
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Awesome Machine Learning Interpretability
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3,241
A curated list of awesome responsible machine learning resources.
Alibi
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2,257
Algorithms for explaining machine learning models
Explainerdashboard
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2,118
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Dalex
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1,242
moDel Agnostic Language for Exploration and eXplanation
Awesome Interpretable Machine Learning
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856
Interpretable_machine_learning_with_python
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629
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Xplique
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522
👋 Xplique is a Neural Networks Explainability Toolbox
Mli Resources
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405
H2O.ai Machine Learning Interpretability Resources
Pyss3
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307
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
Tf Keras Vis
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286
Neural network visualization toolkit for tf.keras
Fcdd
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204
Repository for the Explainable Deep One-Class Classification paper
Whitebox
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184
[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Zennit
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151
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Lrp_for_lstm
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115
Layer-wise Relevance Propagation (LRP) for LSTMs
Rrl
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82
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Cade
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81
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Talktomodel
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68
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
Da_visualization
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62
[CVPR2021] "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai
Imodelsx
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60
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Expmrc
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59
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
Azimuth
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50
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
Xaitk Saliency
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48
An open source, Explainable AI (XAI) framework and toolkit for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.
Xplainable
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48
Real-time explainable machine learning for business optimisation
Torchprism
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40
Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework
Concept Based Xai
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37
Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
Ceml
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35
CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox
Yolo Heatmaps
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32
A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).
Mindsdb_native
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29
Machine Learning in one line of code
Mace
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29
Model Agnostic Counterfactual Explanations
Cem
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26
Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off"
Contrxt
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19
a tool for comparing the predictions of any text classifiers
Diabetes_use_case
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19
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Xai Demonstrator
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17
The XAI Demonstrator is a modular platform that lets users interact with production-grade Explainable AI (XAI) systems.
Pyxai
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17
PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).
Interpretable Ml
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17
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Agrum
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17
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
Xai Tool4gee
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16
A Colab notebook for land cover mapping and monitoring using Earth Engine
Dlime_experiments
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16
In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Lri
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15
[ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.
Pyexplainer
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15
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)
Pipnet
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15
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
Pyreal
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15
Library for generating human-readable machine learning explanations
Ablation
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15
Evaluating XAI methods through ablation studies.
Merlin
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14
MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.
Remembering For The Right Reasons
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14
Official Implementation of Remembering for the Right Reasons (ICLR 2021)
Dnn Inference
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13
[TNNLS 2022] Significance tests of feature relevance for a black-box learner
Trustyai Explainability
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13
TrustyAI Explainability Toolkit
Imodels Experiments
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13
Experiments with experimental rule-based models to go along with imodels.
Reveal2revise
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13
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models. Paper presented at MICCAI 2023 conference.
Clinical Rule Vetting
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12
Learning clinical-decision rules with interpretable models.
Mindsdb_server
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12
MindsDB server allows you to consume and expose MindsDB workflows, through http.
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".
Article Information 2019
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10
Article for Special Edition of Information: Machine Learning with Python
Reasoning Shortcuts
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10
Codebase for the paper: Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Waffle_utils
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10
🧇Waffle Utils🧇 is a set of utilities for waffle components
Protein Ligand Gnn
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10
This repository contains the code for the work on protein-ligand interaction with GNNs and XAI
Shapiq
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9
SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.
Slisemap
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9
SLISEMAP: Combining supervised dimensionality reduction with local explanations
Jsm_2018_paper
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8
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlinepro
Gex
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8
Official code implementation of "GEX: A flexible method for approximating influence via Geometric Ensemble" (NeurIPS 2023)
Virelay
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8
ViRelAy is a visualization tool for the analysis of data as generated by CoRelAy.
Illustrations
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8
This repository contains illustrations to explain concepts in data (science).
Lmdiff
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7
A diff tool for language models
Doxpy
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7
Replication package for the KNOSYS paper titled "An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability".
Counterfactual Explanation Based On Gradual Construction For Deep Networks
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6
Counterfactual Explanation Based on Gradual Construction for Deep Networks Pytorch
Skewedphillips
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6
Työttömyyden ennustamisen työkalu, joka hyödyntää kuluttajahintaindeksejä. Tool for predicting unemployment with consumer price indexes and machine learning.
Torchlrp
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5
A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).
Visualime
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5
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
Xi Method
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5
Xi method
Explainingtitanic
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5
A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc
Car Classification
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5
Visual car recognition using ResNet-based transfer learning
Functional_attribution
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5
Code of our accepted ICML 2021 paper "Towards Rigorous Interpretations: a Formalisation of Feature Attribution" (D. Afchar, R. Hennequin, V. Guigue)
Keras Explainable
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5
Efficient explaining AI algorithms for Keras models
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