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Interpretable Machine Learning

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보존된 평가 기록: 2

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/intelligence-csd-auth-gr/LionLearn/tree/master/LioNets_V2

공식 코드

LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information

https://github.com/interpretml/gam-changer

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GAM Changer: Editing Generalized Additive Models with Interactive Visualization

https://github.com/interpretml/interpret

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GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

https://github.com/isvy08/aim

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An Additive Instance-Wise Approach to Multi-class Model Interpretation

https://github.com/isvy08/l2c

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Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations

https://github.com/ixa-ehu/antidote-casimedicos

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CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures

https://github.com/iyanush/yosm

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yosm: A new yoruba sentiment corpus for movie reviews

https://github.com/jairsan/vivesdebate-speech

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VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

https://github.com/jdkong/gender_recognition

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Twitter-Based Gender Recognition Using Transformers

https://github.com/jeevesh8/arg_mining

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Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?

https://github.com/joelniklaus/lawinstruct

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LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain

https://github.com/jspieler/QBAF-Learning

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Learning Gradual Argumentation Frameworks using Genetic Algorithms

https://github.com/juliaherbinger/repid

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REPID: Regional Effect Plots with implicit Interaction Detection

https://github.com/kaikerochaalves/NFISiS_PyPi

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NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting

https://github.com/kbunte/geodesiclvq_toolbox

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Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

https://github.com/kellygong/camilla

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Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms

https://github.com/keshav22bansal/BAKSA_IITK

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BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Attention for Sentiment Analysis of Code Mixed Text

https://github.com/kexinhuang12345/CASTER

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CASTER: Predicting Drug Interactions with Chemical Substructure Representation

https://github.com/kimkim00/UIT-ViSD4SA

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Span Detection for Aspect-Based Sentiment Analysis in Vietnamese

https://github.com/kseverso/contrastive-LVM

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Unsupervised learning with contrastive latent variable models

https://github.com/kuehnram/findyourfigure

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Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration

https://github.com/lcs2-iiitd/desyr-cikm-2021

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DESYR: Definition and Syntactic Representation Based Claim Detection on the Web

https://github.com/learninginvision/codegan

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CoDeGAN: Contrastive Disentanglement for Generative Adversarial Network

https://github.com/lemeln/nam

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Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/listar2000/gfn-sr

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GFN-SR: Symbolic Regression with Generative Flow Networks

https://github.com/liyingcheng95/iam

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IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

https://github.com/lstate/explainability-in-practice

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Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal

https://github.com/lswhim/icdm

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Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems

https://github.com/lt-nlp-lab-unibo/bert-natural-explanations

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Combining Transformers with Natural Language Explanations

https://github.com/ltgoslo/norec

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NoReC: The Norwegian Review Corpus

https://github.com/luis-armando-perez-rey/lsbd-vae

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Quantifying and Learning Linear Symmetry-Based Disentanglement

https://github.com/lyrain2001/kernel-banzhaf

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Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values

https://github.com/lzswangjian/THGRL

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Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning

https://github.com/m-monaci/MARGOT

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Margin Optimal Classification Trees

https://github.com/m-nauta/pipnet

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Interpreting and Correcting Medical Image Classification with PIP-Net

https://github.com/maitraishaan/vi-and-iml-medsat

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How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data

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