Topic: interpretable-ai Goto Github
Some thing interesting about interpretable-ai
Some thing interesting about interpretable-ai
interpretable-ai,The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
User: 12wang3
interpretable-ai,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-ai,XAI based human-in-the-loop framework for automatic rule-learning.
User: adaamko
interpretable-ai,Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
User: ajayarunachalam
interpretable-ai,This repository contains an implementation of DISC, an algorithm for learning DFAs for multiclass sequence classification.
User: andrewli77
interpretable-ai,All about explainable AI, algorithmic fairness and more
User: andreysharapov
interpretable-ai,A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI, Trustworthy AI, and Human-Centered AI.
Organization: athenacore
interpretable-ai,Investigating the reproducibility of federated GNN models
User: basiralab
interpretable-ai,A python library to agnostically explain multi-label black-box classifiers (tabular data)
User: cecipani
interpretable-ai,Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
User: chr5tphr
interpretable-ai,AI Division, Reverse Engineering CNN Trojans
Organization: cmu-sei
interpretable-ai,[ICCV 2023] Learning Support and Trivial Prototypes for Interpretable Image Classification
User: cwangrun
interpretable-ai,A toolkit for interpreting and analyzing neural networks (vision)
Organization: deepfx
interpretable-ai,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
interpretable-ai,Modular Python Toolbox for Fairness, Accountability and Transparency Forensics
Organization: fat-forensics
Home Page: https://fat-forensics.org
interpretable-ai,Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions
User: gudovskiy
interpretable-ai,Find the samples, in the test data, on which your (generative) model makes mistakes.
Organization: guidelabs
interpretable-ai,Article for Special Edition of Information: Machine Learning with Python
Organization: h2oai
Home Page: https://www.mdpi.com/journal/information/special_issues/ML_Python
interpretable-ai,H2O.ai Machine Learning Interpretability Resources
Organization: h2oai
interpretable-ai,Fit interpretable models. Explain blackbox machine learning.
Organization: interpretml
Home Page: https://interpret.ml/docs
interpretable-ai,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
interpretable-ai,Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
User: jialinwu17
interpretable-ai,List of papers in the area of Explainable Artificial Intelligence Year wise
User: jnikhilreddy
interpretable-ai,A curated list of awesome responsible machine learning resources.
User: jphall663
interpretable-ai,Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
User: jphall663
interpretable-ai,Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
User: jphall663
interpretable-ai,Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
User: jphall663
interpretable-ai,Explainable AI in Julia.
Organization: julia-xai
interpretable-ai,Explainability of Deep Learning Models
User: koriavinash1
interpretable-ai,Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.
Organization: linkedin
interpretable-ai,SIDU: SImilarity Difference and Uniqueness method for explainable AI
User: marcoparola
interpretable-ai,Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"
User: marcovirgolin
interpretable-ai,Implementation of Beyond Neural Scaling beating power laws for deep models and prototype-based models
User: naotoo1
interpretable-ai,A python package for prototype-based machine learning models
User: naotoo1
Home Page: https://naotoo1.github.io/prosemble/
interpretable-ai,Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
User: navdeep-g
interpretable-ai,Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
User: parantapa
interpretable-ai,PyTorch Explain: Interpretable Deep Learning in Python.
User: pietrobarbiero
interpretable-ai,Concept activation vectors for Keras
User: pnxenopoulos
interpretable-ai,Mechanistic Interpretability Tutorials, Results and research log as I learn from publicly available research, and experimentation.
User: poppingtonic
interpretable-ai,Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN
User: prclibo
interpretable-ai,Model interpretability and understanding for PyTorch
Organization: pytorch
Home Page: https://captum.ai
interpretable-ai,ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in prototype-based machine learning algorithms.
Organization: si-cim
interpretable-ai,The official implementation of AAAI'24 paper: Self-Interpretable Graph Learning with Sufficient and Necessary Explanations.
Organization: sjtu-dmtai
interpretable-ai,In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
User: tootouch
interpretable-ai,NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Organization: uncbiag
interpretable-ai,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-ai,A collection of research materials on explainable AI/ML
User: wangyongjie-ntu
interpretable-ai,A Multimodal Transformer: Fusing Clinical Notes With Structured EHR Data for Interpretable In-Hospital Mortality Prediction
User: weimin17
interpretable-ai,A PyTorch implementation of constrained optimization and modeling techniques
User: willbakst
Home Page: https://willbakst.github.io/pytorch-lattice/
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