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fastshapFast approximate Shapley values in R
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ProtoTreeProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
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mllpThe code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
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datafsmMachine Learning Finite State Machine Models from Data with Genetic Algorithms
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pyCeterisParibusPython library for Ceteris Paribus Plots (What-if plots)
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mindsdb serverMindsDB server allows you to consume and expose MindsDB workflows, through http.
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self critical vqaCode for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
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MindsdbPredictive AI layer for existing databases.
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TensorwatchDebugging, monitoring and visualization for Python Machine Learning and Data Science
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WhiteBox-Part1In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
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global-attribution-mappingGAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations
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SHAP FOLD(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining
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shaprExplaining the output of machine learning models with more accurately estimated Shapley values
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CARLACARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
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path explainA repository for explaining feature attributions and feature interactions in deep neural networks.
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ddsm-visual-primitivesUsing deep learning to discover interpretable representations for mammogram classification and explanation
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GraphLIMEThis is a Pytorch implementation of GraphLIME
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zennitZennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
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Relevance-CAMThe official code of Relevance-CAM
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javaAnchorExplainerExplains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018
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transformers-interpretModel explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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responsible-ai-toolboxThis project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
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hierarchical-dnn-interpretationsUsing / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
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megMolecular Explanation Generator
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dlime experimentsIn 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).
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Transformer-MM-Explainability[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
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concept-based-xaiLibrary implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
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bert attn vizVisualize BERT's self-attention layers on text classification tasks
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expmrcExpMRC: Explainability Evaluation for Machine Reading Comprehension
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3D-GuidedGradCAM-for-Medical-ImagingThis Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.
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graspEssential NLP & ML, short & fast pure Python code
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neuro-symbolic-sudoku-solver⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
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ShapML.jlA Julia package for interpretable machine learning with stochastic Shapley values
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interpretable-mlTechniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
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xai-iml-sotaInteresting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
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Awesome-XAI-EvaluationReference tables to introduce and organize evaluation methods and measures for explainable machine learning systems
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ArenaRData generator for Arena - interactive XAI dashboard
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