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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://bitbucket.org/ghentdatascience/essd_public

공식 코드

Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach

https://bitbucket.org/hl2exe/tcgnn

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Tree-Constrained Graph Neural Networks For Argument Mining

https://git.sr.ht/~hyphaebeast/challenging-xai

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Challenging common interpretability assumptions in feature attribution explanations

https://github.com/3riccc/neural_pruning

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Neural Network Pruning by Gradient Descent

https://github.com/AGalassi/StructurePrediction18

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Multi-Task Attentive Residual Networks for Argument Mining

https://github.com/AccelerationConsortium/gremlin

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Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel

https://github.com/AmirAbaskohi/Persian-Emotion-Detection-using-ParsBERT-and-Imbalanced-Data-Handling-Approaches

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Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches

https://github.com/Armaniii/WIBA

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WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining

https://github.com/ArrasL/LRP_for_LSTM

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Explaining Recurrent Neural Network Predictions in Sentiment Analysis

https://github.com/Arzik1987/SDRE

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REDS: Rule Extraction for Discovering Scenarios

https://github.com/BW297/DFCD

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A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments

https://github.com/Babakbehkamkia/GPT-Stance-Detection

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Zero-Shot Stance Detection using Contextual Data Generation with LLMs

https://github.com/BeanHam/interpretable-machine-learning

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In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

https://github.com/CancerDataScience/NuCLS

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NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

https://github.com/ChaokunChang/Biathlon

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Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines

https://github.com/Chaoukia/branches

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Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*

https://github.com/DIGITLab23/RIGID

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Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

https://github.com/DariusAf/functional_attribution

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Towards Rigorous Interpretations: a Formalisation of Feature Attribution

https://github.com/DavidHason/Causal_Analysis

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Causal Analysis of Customer Churn Using Deep Learning

https://github.com/DiTEC-project/pyaerial

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Neurosymbolic Association Rule Mining from Tabular Data

https://github.com/DillonZChen/goose

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Graph Learning for Numeric Planning

https://github.com/DrImpossible/Sub-word-LSTM

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Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text

https://github.com/Eroica-cpp/YouTube-Statistics

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YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

https://github.com/GDPlumb/ELDR

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Explaining Groups of Points in Low-Dimensional Representations

https://github.com/GDPlumb/ExpO

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Regularizing Black-box Models for Improved Interpretability

https://github.com/GarfieldLyu/NeuralFeatureSelectionLTR

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Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?

https://github.com/HKUST-KnowComp/KnowComp-DialAM2024-ACL2024

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KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining

https://github.com/HMProenca/RuleList

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Discovering outstanding subgroup lists for numeric targets using MDL

https://github.com/Hellisotherpeople/DebateSum

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DebateSum: A large-scale argument mining and summarization dataset

https://github.com/Hellisotherpeople/debate2vec

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DebateSum: A large-scale argument mining and summarization dataset

https://github.com/IBCNServices/GENESIM

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GENESIM: genetic extraction of a single, interpretable model

https://github.com/Jakob-Bach/Constrained-Subgroup-Discovery

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Using Constraints to Discover Sparse and Alternative Subgroup Descriptions

https://github.com/JeremCab/AMwithGPT

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In-Context Learning and Fine-Tuning GPT for Argument Mining

https://github.com/JianqiaoMao/Interpretable-machine-learning-for-HVAC-predictive-control

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Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic Buildings

https://github.com/Jimmy-Lin/GeneralizedOptimalSparseDecisionTrees

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Generalized and Scalable Optimal Sparse Decision Trees

https://github.com/LCS2-IIITD/LESA-EACL-2021

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LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content

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