Topic: interpretable-machine-learning Goto Github
Some thing interesting about interpretable-machine-learning
Some thing interesting about interpretable-machine-learning
interpretable-machine-learning,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
interpretable-machine-learning,XAI based human-in-the-loop framework for automatic rule-learning.
User: adaamko
interpretable-machine-learning,All about explainable AI, algorithmic fairness and more
User: andreysharapov
interpretable-machine-learning,Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
User: atulshanbhag
interpretable-machine-learning,Fast approximate Shapley values in R
User: bgreenwell
Home Page: https://bgreenwell.github.io/fastshap/
interpretable-machine-learning,Information Bottlenecks for Attribution
Organization: bioroboticslab
interpretable-machine-learning,Tutorial on the Convolutional Tsetlin Machine
Organization: cair
Home Page: https://arxiv.org/abs/1905.09688
interpretable-machine-learning,Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.
Organization: cair
Home Page: https://pypi.org/project/pyTsetlinMachineParallel/
interpretable-machine-learning,Model Agnostic Counterfactual Explanations
Organization: charmlab
interpretable-machine-learning,[HELP REQUESTED] Generalized Additive Models in Python
User: dswah
Home Page: https://pygam.readthedocs.io
interpretable-machine-learning,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/
interpretable-machine-learning,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
Home Page: https://www.explainx.ai
interpretable-machine-learning,FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
User: firmai
Home Page: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3619715
interpretable-machine-learning,An Open-Source Library for the interpretability of time series classifiers
Organization: fzi-forschungszentrum-informatik
interpretable-machine-learning,Quantified Sleep: Machine learning techniques for observational n-of-1 studies.
User: gianlucatruda
interpretable-machine-learning,[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
Organization: graph-com
Home Page: https://arxiv.org/abs/2201.12987
interpretable-machine-learning,H2O.ai Machine Learning Interpretability Resources
Organization: h2oai
interpretable-machine-learning,💡 Adversarial attacks on explanations and how to defend them
User: hbaniecki
Home Page: https://doi.org/10.1016/j.inffus.2024.102303
interpretable-machine-learning,🕵️♂️ Interpreting Convolutional Neural Network (CNN) Results.
Organization: idealo
Home Page: https://speakerdeck.com/tanujjain/demystifying-the-neural-network-black-box
interpretable-machine-learning,Implementation of the paper "Shapley Explanation Networks"
Organization: inouye-lab
interpretable-machine-learning,Generate Diverse Counterfactual Explanations for any machine learning model.
Organization: interpretml
Home Page: https://interpretml.github.io/DiCE/
interpretable-machine-learning,Fit interpretable models. Explain blackbox machine learning.
Organization: interpretml
Home Page: https://interpret.ml/docs
interpretable-machine-learning,A list of (post-hoc) XAI for time series
User: jhoelli
interpretable-machine-learning,A curated list of awesome responsible machine learning resources.
User: jphall663
interpretable-machine-learning,Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
User: jphall663
interpretable-machine-learning,🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease
User: jrieke
interpretable-machine-learning,
User: lopusz
interpretable-machine-learning,PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
User: m-nauta
interpretable-machine-learning,ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
User: m-nauta
interpretable-machine-learning,Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
Organization: ml-for-high-risk-apps-book
interpretable-machine-learning,moDel Agnostic Language for Exploration and eXplanation
Organization: modeloriented
Home Page: https://dalex.drwhy.ai
interpretable-machine-learning,📍 Interactive Studio for Explanatory Model Analysis
Organization: modeloriented
Home Page: https://doi.org/10.1007/s10618-023-00924-w
interpretable-machine-learning,Explainable Machine Learning in Survival Analysis
Organization: modeloriented
Home Page: https://modeloriented.github.io/survex
interpretable-machine-learning,Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Organization: modeloriented
Home Page: https://modeloriented.github.io/treeshap/
interpretable-machine-learning,An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
User: nredell
interpretable-machine-learning,A Julia package for interpretable machine learning with stochastic Shapley values
User: nredell
Home Page: https://nredell.github.io/ShapML.jl/dev/
interpretable-machine-learning,Interesting resources related to XAI (Explainable Artificial Intelligence)
User: pbiecek
interpretable-machine-learning,Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
User: pbiecek
Home Page: http://tiny.cc/eRum2020
interpretable-machine-learning,PyTorch Explain: Interpretable Deep Learning in Python.
User: pietrobarbiero
interpretable-machine-learning,An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization
User: rachtibat
Home Page: https://www.nature.com/articles/s42256-023-00711-8
interpretable-machine-learning,Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
User: rehmanzafar
interpretable-machine-learning,PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Organization: salesforce
interpretable-machine-learning,OmniXAI: A Library for eXplainable AI
Organization: salesforce
interpretable-machine-learning,
User: samyu0304
interpretable-machine-learning,PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
Organization: selfexplainml
Home Page: https://selfexplainml.github.io/PiML-Toolbox
interpretable-machine-learning,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
interpretable-machine-learning,Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems
User: sinamohseni
interpretable-machine-learning,Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.
User: vincentgranville
Home Page: https://mltechniques.com/product/intuitive-machine-learning/
interpretable-machine-learning,Concept Bottleneck Models, ICML 2020
User: yewsiang
interpretable-machine-learning,Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"
Organization: zju-vipa
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.