Topic: interpretability Goto Github
Some thing interesting about interpretability
Some thing interesting about interpretability
interpretability,OpenXAI : Towards a Transparent Evaluation of Model Explanations
Organization: ai4life-group
Home Page: https://open-xai.github.io/
interpretability,Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet
User: alvinwan
Home Page: https://nbdt.aaalv.in
interpretability,Human-explainable AI.
Organization: bcg-x-official
Home Page: https://bcg-x-official.github.io/facet
interpretability,Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
User: cdpierse
interpretability,FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
User: chaoyanghe
interpretability,Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
User: csinva
Home Page: https://csinva.io/imodels
interpretability,A curated list of awesome Fairness in AI resources
Organization: datamllab
interpretability,👋 Xplique is a Neural Networks Explainability Toolbox
Organization: deel-ai
Home Page: https://deel-ai.github.io/xplique
interpretability,A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Organization: ethicalml
Home Page: https://ethical.institute/principles.html
interpretability,XAI - An eXplainability toolbox for machine learning
Organization: ethicalml
Home Page: https://ethical.institute/principles.html#commitment-3
interpretability,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
interpretability,Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
User: frgfm
Home Page: https://frgfm.github.io/torch-cam/
interpretability,A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Organization: google
interpretability,H2O.ai Machine Learning Interpretability Resources
Organization: h2oai
interpretability,💡 Adversarial attacks on explanations and how to defend them
User: hbaniecki
Home Page: https://doi.org/10.1016/j.inffus.2024.102303
interpretability,[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.
User: hila-chefer
interpretability,For calculating global feature importance using Shapley values.
User: iancovert
interpretability,Interpretability for sequence generation models 🐛 🔍
Organization: inseq-team
Home Page: https://inseq.org
interpretability,Fit interpretable models. Explain blackbox machine learning.
Organization: interpretml
Home Page: https://interpret.ml/docs
interpretability,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
interpretability,Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
User: joaolages
interpretability,A curated list of awesome responsible machine learning resources.
User: jphall663
interpretability,Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
User: jphall663
interpretability,Locating and editing factual associations in GPT (NeurIPS 2022)
User: kmeng01
Home Page: https://rome.baulab.info
interpretability,Public facing deeplift repo
Organization: kundajelab
interpretability,Fast SHAP value computation for interpreting tree-based models
Organization: linkedin
interpretability,🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Organization: maif
Home Page: https://maif.github.io/shapash/
interpretability,Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Organization: microsoft
Home Page: https://responsibleaitoolbox.ai/
interpretability,Visualization toolkit for neural networks in PyTorch! Demo -->
User: misaogura
Home Page: https://youtu.be/18Iw4qYqfPo
interpretability,moDel Agnostic Language for Exploration and eXplanation
Organization: modeloriented
Home Page: https://dalex.drwhy.ai
interpretability,📍 Interactive Studio for Explanatory Model Analysis
Organization: modeloriented
Home Page: https://doi.org/10.1007/s10618-023-00924-w
interpretability,深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)
User: onetaken
interpretability,Interesting resources related to XAI (Explainable Artificial Intelligence)
User: pbiecek
interpretability,The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
User: pratyushasharma
Home Page: https://pratyushasharma.github.io/laser/
interpretability,Model interpretability and understanding for PyTorch
Organization: pytorch
Home Page: https://captum.ai
interpretability,[ICCV 2017] Torch code for Grad-CAM
User: ramprs
Home Page: https://arxiv.org/abs/1610.02391
interpretability,💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Organization: scalaconsultants
interpretability,Algorithms for explaining machine learning models
Organization: seldonio
Home Page: https://docs.seldon.io/projects/alibi/en/stable/
interpretability,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
interpretability,A game theoretic approach to explain the output of any machine learning model.
Organization: shap
Home Page: https://shap.readthedocs.io
interpretability,A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
User: shubhomoydas
interpretability,Interpretability Methods for tf.keras models with Tensorflow 2.x
Organization: sicara
Home Page: https://tf-explain.readthedocs.io
interpretability,Stanford NLP Python Library for Understanding and Improving PyTorch Models via Interventions
Organization: stanfordnlp
Home Page: https://arxiv.org/abs/2403.07809
interpretability,StellarGraph - Machine Learning on Graphs
Organization: stellargraph
Home Page: https://stellargraph.readthedocs.io/
interpretability,A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Organization: tensorflow
interpretability,A collection of infrastructure and tools for research in neural network interpretability.
Organization: tensorflow
interpretability,Code for the TCAV ML interpretability project
Organization: tensorflow
interpretability,Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
Organization: understandable-machine-intelligence-lab
Home Page: https://quantus.readthedocs.io/
interpretability,A collection of research materials on explainable AI/ML
User: wangyongjie-ntu
interpretability,CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
Organization: xmed-lab
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.