Topic: interpretable-ml Goto Github
Some thing interesting about interpretable-ml
Some thing interesting about interpretable-ml
interpretable-ml,The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
User: 12wang3
interpretable-ml,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-ml,Overview of machine learning interpretation techniques and their implementations
User: akifcinar
interpretable-ml,Source code for PIP: Pictorial Interpretable Prototype Learning for Time Series Classification
User: alirezaghods
Home Page: https://alirezaghods.github.io/
interpretable-ml,This repository contains an implementation of DISC, an algorithm for learning DFAs for multiclass sequence classification.
User: andrewli77
interpretable-ml,Genetic programming method for explaining complex black-box models
User: benjaminpatrickevans
Home Page: https://dl.acm.org/citation.cfm?id=3321707.3321726
interpretable-ml,A list of research papers of explainable machine learning.
User: birkhoffg
interpretable-ml,JAX-based Model Explanation and Interpretation Library
User: birkhoffg
interpretable-ml,XAI-Tris
Organization: braindatalab
interpretable-ml,A python library to agnostically explain multi-label black-box classifiers (tabular data)
User: cecipani
interpretable-ml,Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
User: chr5tphr
interpretable-ml,Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
Organization: csailvision
interpretable-ml,Getting explanations for predictions made by black box models.
User: datatrigger
Home Page: https://blog.vlgdata.io/post/interpretable_machine_learning_shap/
interpretable-ml,[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
Organization: deep-symbolic-mathematics
Home Page: https://openreview.net/forum?id=0rVXQEeFEL
interpretable-ml,XLabel: An Explainable Data Labeling Assistant
User: donlapark
interpretable-ml,Interpretability and Fairness in Machine Learning
User: fpretto
interpretable-ml,An Open-Source Library for the interpretability of time series classifiers
Organization: fzi-forschungszentrum-informatik
interpretable-ml,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-ml,H2O.ai Machine Learning Interpretability Resources
Organization: h2oai
interpretable-ml,Fit interpretable models. Explain blackbox machine learning.
Organization: interpretml
Home Page: https://interpret.ml/docs
interpretable-ml,Interpretable AI with Safeguard AI (paper study, implement-code review)
User: jihunlee326
interpretable-ml,A curated list of awesome responsible machine learning resources.
User: jphall663
interpretable-ml,Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
User: jphall663
interpretable-ml,Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
User: jphall663
interpretable-ml,Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
User: jphall663
interpretable-ml,Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
User: jphall663
interpretable-ml,explainable and interpretable methods for AI and data science
User: lorarjohns
interpretable-ml,PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
User: m-nauta
interpretable-ml,Explaining Model Behavior with Global Causal Analysis
User: marcelrobeer
interpretable-ml,A baseline genetic algorithm for the discovery of counterfactuals, implemented in Python for ease of use and heavily leveraging NumPy for speed.
User: marcovirgolin
interpretable-ml,High-Performance Symbolic Regression in Python and Julia
User: milescranmer
Home Page: https://astroautomata.com/PySR
interpretable-ml,Distributed High-Performance Symbolic Regression in Julia
User: milescranmer
Home Page: https://astroautomata.com/SymbolicRegression.jl/dev/
interpretable-ml,Explainable Machine Learning in Survival Analysis
Organization: modeloriented
Home Page: https://modeloriented.github.io/survex
interpretable-ml,Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
User: navdeep-g
interpretable-ml,Optimizing Mind static website v1
User: optimizingmind
interpretable-ml,Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
User: parantapa
interpretable-ml,Interpretable machine learning algorithm
Organization: plantedml
interpretable-ml,Random Planted Forest
Organization: plantedml
Home Page: http://plantedml.com/randomPlantedForest/
interpretable-ml,Concept activation vectors for Keras
User: pnxenopoulos
interpretable-ml,Model interpretability and understanding for PyTorch
Organization: pytorch
Home Page: https://captum.ai
interpretable-ml,Pytorch-based tools for constructing a vocabulary of visual concepts in a GAN.
User: schwettmann
Home Page: https://visualvocab.csail.mit.edu
interpretable-ml,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-ml,Tutorial on Representer Point Selection for Explaining Deep Neural Networks (CIFAR-10)
User: shinkyuy
interpretable-ml,Create Interpretable Machine Learning plots with an interactive Shiny based dashboard
Organization: slds-lmu
interpretable-ml,Investigate BERT on Non-linearity and Layer Commutativity
User: sumuzhao
interpretable-ml,A PyTorch implementation of constrained optimization and modeling techniques
User: willbakst
Home Page: https://willbakst.github.io/pytorch-lattice/
interpretable-ml,Optimal Sparse Decision Trees
User: xiyanghu
interpretable-ml,XMLX GitHub configuration
Organization: xmlx-io
Home Page: https://github.com/xmlx-io
interpretable-ml,Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.
User: zalkikar
Home Page: https://pypi.org/project/mlm-bias
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