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Interpretable Machine Learning

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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/chaoukia/thompson-sampling-decision-trees

공식 코드

Online Learning of Decision Trees with Thompson Sampling

https://github.com/chengstark/interp.-eeg-public

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Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning

https://github.com/clarkdinh/chusp

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Mining compact high utility sequential patterns

https://github.com/compstat-lmu/paper_2019_iml_measures

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Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

https://github.com/computationalmedia/compsumm

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Comparative Document Summarisation via Classification

https://github.com/corradomonti/learnable-opinion-dynamics

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Learning Opinion Dynamics From Social Traces

https://github.com/cruiseresearchgroup/AuxMobLCast

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Leveraging Language Foundation Models for Human Mobility Forecasting

https://github.com/csinva/disentangled-attribution-curves

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Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees

https://github.com/csinva/hierarchical-dnn-interpretations

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Hierarchical interpretations for neural network predictions

https://github.com/csinva/imodels

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Interpretable machine learning: definitions, methods, and applications

https://github.com/csinva/imodelsX

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Rethinking Interpretability in the Era of Large Language Models

https://github.com/cslijt/id-cdf

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Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm

https://github.com/cvg/lightglue

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LightGlue: Local Feature Matching at Light Speed

https://github.com/cwangrun/CIPL

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Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation

https://github.com/cwangrun/MGProto

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Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition

https://github.com/cwangrun/ST-ProtoPNet

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Learning Support and Trivial Prototypes for Interpretable Image Classification

https://github.com/damo-nlp-sg/llm-argumentation

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Exploring the Potential of Large Language Models in Computational Argumentation

https://github.com/dassein/cycloid_em_mlr

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Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression

https://github.com/data-sciencehub/gpce-sensitivity

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Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models

https://github.com/david-rundel/tabpfn_iml

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Interpretable Machine Learning for TabPFN

https://github.com/ddfan/balsa

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Bayesian Learning-Based Adaptive Control for Safety Critical Systems

https://github.com/dechrist2021/mulomba

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Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource Settings

https://github.com/deep-symbolic-mathematics/llm-sr

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LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

https://github.com/deezer/interpretable_nn_attribution

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Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction

https://github.com/dengyang17/OAAG

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Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce

https://github.com/dependable-intelligent-systems-lab/xwhy

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Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations

https://github.com/devilyangs/emo-nas-cd

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Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

https://github.com/ditec-project/aerial-rule-mining

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Neurosymbolic Association Rule Mining from Tabular Data

https://github.com/dmelis/interpretwoe

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From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence

https://github.com/dsfsi/sa-fake-news-2020

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Is it Fake? News Disinformation Detection on South African News Websites

https://github.com/dsi-lab1/huspm

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HUSP-SP: Faster Utility Mining on Sequence Data

https://github.com/eclique/RISE

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RISE: Randomized Input Sampling for Explanation of Black-box Models

https://github.com/ecnu-ilog/lrcd

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Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis

https://github.com/engsalem/arglegalsumm

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ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

https://github.com/epochx/opinatt

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Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN

https://github.com/epochx/opminreplicability

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Replication issues in syntax-based aspect extraction for opinion mining

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