#计算机科学#Fit interpretable models. Explain blackbox machine learning.
翻译 - 拟合可解释的模型。说明黑匣子机器学习。
Model interpretability and understanding for PyTorch
翻译 - PyTorch的模型可解释性和理解
#Awesome#A curated list of awesome responsible machine learning resources.
#算法刷题#High-Performance Symbolic Regression in Python and Julia
#计算机科学#Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
翻译 - 基于Pytorch的工具,用于可视化和理解GAN神经元。 https://gandissect.csail.mit.edu/
#计算机科学#Distributed High-Performance Symbolic Regression in Julia
#计算机科学#Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
#计算机科学#H2O.ai Machine Learning Interpretability Resources
#自然语言处理#A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )
#计算机科学#Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
#时序数据库#An Open-Source Library for the interpretability of time series classifiers
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 Classif...
#计算机科学#Explainable Machine Learning in Survival Analysis
#算法刷题#Optimal Sparse Decision Trees
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
#计算机科学#[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
#计算机科学#A list of research papers of explainable machine learning.
A PyTorch implementation of constrained optimization and modeling techniques
#计算机科学#Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/