Topic: xai Goto Github
Some thing interesting about xai
Some thing interesting about xai
xai,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"
User: 12wang3
xai,Awesome Explainable AI (XAI) and Interpretable ML Papers and Resources
Organization: altamiracorp
xai,Layer-wise Relevance Propagation (LRP) for LSTMs.
User: arrasl
xai,Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Organization: astrazeneca
xai,Fast approximate Shapley values in R
User: bgreenwell
Home Page: https://bgreenwell.github.io/fastshap/
xai,Model Agnostic Counterfactual Explanations
Organization: charmlab
xai,Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
User: chr5tphr
xai,Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
User: csinva
Home Page: https://csinva.io/imodelsX/
xai,A curated list of awesome Fairness in AI resources
Organization: datamllab
xai,👋 Xplique is a Neural Networks Explainability Toolbox
Organization: deel-ai
Home Page: https://deel-ai.github.io/xplique
xai,Visualization tool for Graph Neural Networks
Organization: dmlc
xai,TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
User: dylan-slack
Home Page: https://nlp.ics.uci.edu/talk-to-healthcare-model/
xai,Detect model's attention
User: eclique
xai,XAI - An eXplainability toolbox for machine learning
Organization: ethicalml
Home Page: https://ethical.institute/principles.html#commitment-3
xai,Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]
Organization: explainx
xai,A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).
User: fhvilshoj
xai,Papers about explainability of GNNs
User: flyingdoog
xai,[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
Organization: graph-com
Home Page: https://arxiv.org/abs/2201.12987
xai,H2O.ai Machine Learning Interpretability Resources
Organization: h2oai
xai,COVID-Net Open Source Initiative - Models and Data for COVID-19 Detection in Chest CT
User: haydengunraj
Home Page: https://alexswong.github.io/COVID-Net/
xai,💡 Adversarial attacks on explanations and how to defend them
User: hbaniecki
Home Page: https://doi.org/10.1016/j.inffus.2024.102303
xai,[CVPR2021] "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai
User: hou-yz
xai,Generate Diverse Counterfactual Explanations for any machine learning model.
Organization: interpretml
Home Page: https://interpretml.github.io/DiCE/
xai,Fit interpretable models. Explain blackbox machine learning.
Organization: interpretml
Home Page: https://interpret.ml/docs
xai,Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
User: jacobgil
Home Page: https://jacobgil.github.io/pytorch-gradcam-book
xai,A curated list of awesome responsible machine learning resources.
User: jphall663
xai,Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
User: jphall663
xai,Explainable AI in Julia.
Organization: julia-xai
xai,Neural network visualization toolkit for tf.keras
User: keisen
Home Page: https://keisen.github.io/tf-keras-vis-docs/
xai,Repository for the Explainable Deep One-Class Classification paper
User: liznerski
xai,SurvSHAP(t): Time-dependent explanations of machine learning survival models
Organization: mi2datalab
Home Page: https://doi.org/10.1016/j.knosys.2022.110234
xai,moDel Agnostic Language for Exploration and eXplanation
Organization: modeloriented
Home Page: https://dalex.drwhy.ai
xai,Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)
Organization: modeloriented
Home Page: https://ModelOriented.github.io/iBreakDown/
xai,📍 Interactive Studio for Explanatory Model Analysis
Organization: modeloriented
Home Page: https://doi.org/10.1007/s10618-023-00924-w
xai,Explainable Machine Learning in Survival Analysis
Organization: modeloriented
Home Page: https://modeloriented.github.io/survex
xai,Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Organization: modeloriented
Home Page: https://modeloriented.github.io/treeshap/
xai,Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
User: oegedijk
Home Page: http://explainerdashboard.readthedocs.io
xai,Model Agnostics breakDown plots
User: pbiecek
Home Page: https://pbiecek.github.io/breakDown/
xai,Explanatory Model Analysis. Explore, Explain and Examine Predictive Models
User: pbiecek
Home Page: http://ema.drwhy.ai
xai,Interesting resources related to XAI (Explainable Artificial Intelligence)
User: pbiecek
xai,Algorithms for explaining machine learning models
Organization: seldonio
Home Page: https://docs.seldon.io/projects/alibi/en/stable/
xai,A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
User: sergioburdisso
Home Page: https://pyss3.readthedocs.io
xai,[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Organization: squaredev-io
Home Page: https://squaredev.io/whitebox/
xai,Interpretability and explainability of data and machine learning models
Organization: trusted-ai
Home Page: https://aix360.res.ibm.com/
xai,Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
Organization: understandable-machine-intelligence-lab
Home Page: https://quantus.readthedocs.io/
xai,A collection of research materials on explainable AI/ML
User: wangyongjie-ntu
xai,Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
User: whyisyoung
Home Page: https://liminyang.web.illinois.edu
xai,As part of the Explainable AI Toolkit (XAITK), XAITK-Saliency is an open source, explainable AI framework for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.
Organization: xaitk
Home Page: https://xaitk.org
xai,Pytorch Implementation of recent visual attribution methods for model interpretability
User: yulongwang12
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