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Interpretable Machine Learning

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하위 태스크: 2
평가표: 1
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/soumickmj/TorchEsegeta

공식 코드

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

https://github.com/soumickmj/diagnoPP

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Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

https://github.com/stevekgyang/bhg

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A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge

https://github.com/subgroupawarevae/savae

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Subgroup Discovery in Unstructured Data

https://github.com/suinleelab/attributionpriors

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Improving performance of deep learning models with axiomatic attribution priors and expected gradients

https://github.com/sukrutrao/attribution-evaluation

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Towards Better Understanding Attribution Methods

https://github.com/sylydya/Consistent-Sparse-Deep-Learning-Theory-and-Computation

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Consistent Sparse Deep Learning: Theory and Computation

https://github.com/szandala/TorchPRISM

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TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualization

https://github.com/talismanbrandi/covid-19-USA-SE

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Socio-economic disparities and COVID-19 in the USA

https://github.com/tataganesh/hrv-edgedevice

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Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices

https://github.com/tchewik/e2e-microtexts

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End-to-End Argument Mining over Varying Rhetorical Structures

https://github.com/thomasnorr/q-senn

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Take 5: Interpretable Image Classification with a Handful of Features

https://github.com/thu-keg/mooc-radar

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MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs

https://github.com/tom1092/optimal-logistic-classification-trees

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Loss-Optimal Classification Trees: A Generalized Framework and the Logistic Case

https://github.com/tomatenmarc/taco

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TACO -- Twitter Arguments from COnversations

https://github.com/tridungduong16/fairce

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CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows

https://github.com/tridungduong16/multiobj-scm-cf

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Causality-based Counterfactual Explanation for Classification Models

https://github.com/trtm/ABAM

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Aspect-Based Argument Mining

https://github.com/trtm/AURC

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Fine-Grained Argument Unit Recognition and Classification

https://github.com/trusthlt/mining-legal-arguments

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Mining Legal Arguments in Court Decisions

https://github.com/tuhinjubcse/AMPERSAND-EMNLP2019

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AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

https://github.com/tunazislam/bma-fb-ad-climate

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Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging

https://github.com/ubc-systopia/gosdt-guesses

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Fast Sparse Decision Tree Optimization via Reference Ensembles

https://github.com/ubc-systopia/pydl8.5-lbguess

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Fast Sparse Decision Tree Optimization via Reference Ensembles

https://github.com/ubc-systopia/tree-benchmark

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Fast Sparse Decision Tree Optimization via Reference Ensembles

https://github.com/udellgroup/controlburn

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ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles

https://github.com/uhh-lt/comparative

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Categorizing Comparative Sentences

https://github.com/ukplab/acl2024-ood-compuational-argumentation

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How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study

https://github.com/ukplab/argument-topic-diversity

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Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets

https://github.com/unikcc/diaasq

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DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis

https://github.com/ustunb/slim_for_matlab

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Supersparse Linear Integer Models for Optimized Medical Scoring Systems

https://github.com/vamsi995/paraphrase-generator

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DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models

https://github.com/vaslyb/perceptible-music-tagging

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Perceptual Musical Features for Interpretable Audio Tagging

https://github.com/vene/marseille

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Argument Mining with Structured SVMs and RNNs

https://github.com/veranchos/argmining_tweets

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Data and models for stance and premise detection in COVID-19 tweets: insights from the Social Media Mining for Health (SMM4H) 2022 shared task

https://github.com/vidalt/BA-Trees

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Born-Again Tree Ensembles

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