Topic: interpretability Goto Github
Some thing interesting about interpretability
Some thing interesting about interpretability
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://ethicalml.github.io/awesome-production-machine-learning
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
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 JAX research toolkit for building, editing, and visualizing neural networks.
Organization: google-deepmind
Home Page: https://penzai.readthedocs.io/
interpretability,A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Organization: google
Home Page: https://ydf.readthedocs.io/
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,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,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,The nnsight package enables interpreting and manipulating the internals of deep learned models.
Organization: ndif-team
Home Page: https://nnsight.net/
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,ReFT: Representation Finetuning for Language Models
Organization: stanfordnlp
Home Page: https://arxiv.org/abs/2404.03592
interpretability,Stanford NLP Python Library for Understanding and Improving PyTorch Models via Interventions
Organization: stanfordnlp
Home Page: http://pyvene.ai
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
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