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Interpretable Machine Learning
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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/LegalAM/JDMDH-2022-ECHR
공식 코드Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks
https://github.com/LukasMut/Subjective_QA
공식 코드https://github.com/M-Nauta/ProtoTree
공식 코드Neural Prototype Trees for Interpretable Fine-grained Image Recognition
https://github.com/MIMBCD-UI/prototype-multi-modality
공식 코드BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
https://github.com/MilesCranmer/PySR
공식 코드Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
https://github.com/ModelOriented/triplot
공식 코드https://github.com/Mythreyi-V/three-phase-fidelity-evaluation
공식 코드Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
https://github.com/NKU-IIPLab/BMRC
공식 코드Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
https://github.com/NLPfreshman0/PSCRF
공식 코드Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis
https://github.com/Nicholas-Simafranca/Super_Learner_Wild_Fire
공식 코드Modelling wildland fire burn severity in California using a spatial Super Learner approach
https://github.com/NicoHambauer/Model-Performance-vs-Interpretability
공식 코드https://github.com/PAIR-code/saliency
공식 코드https://github.com/PhungLai728/OnML
공식 코드Ontology-based Interpretable Machine Learning for Textual Data
https://github.com/PierreBlanchart/CMBDTC
공식 코드An exact counterfactual-example-based approach to tree-ensemble models interpretability
https://github.com/ReCAP-UTR/Argument-Graph-Mining
공식 코드Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs
https://github.com/RemilYoucef/split-sd4x
공식 코드Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery
https://github.com/STAC-USC/DeepNNK_polytope_interpolation
공식 코드DeepNNK: Explaining deep models and their generalization using polytope interpolation
https://github.com/SeojinBang/VIBI
공식 코드Explaining a black-box using Deep Variational Information Bottleneck Approach
https://github.com/SinaMohseni/ML-Interpretability-Evaluation-Benchmark
공식 코드A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning
https://github.com/SkBlaz/SCD
공식 코드https://github.com/Spencerfar/djin-aging
공식 코드Interpretable machine learning for high-dimensional trajectories of aging health
https://github.com/UKPLab/acl2019-BERT-argument-classification-and-clustering
공식 코드Classification and Clustering of Arguments with Contextualized Word Embeddings
https://github.com/UKPLab/acl2021-label-suggestions-german-covid19
공식 코드Investigating label suggestions for opinion mining in German Covid-19 social media
https://github.com/UKPLab/arxiv2018-bayesian-ensembles
공식 코드https://github.com/UKPLab/emnlp2017-claim-identification
공식 코드What is the Essence of a Claim? Cross-Domain Claim Identification
https://github.com/Yale-LILY/ConvoSumm
공식 코드https://github.com/YilunZhou/feature-attribution-evaluation
공식 코드Do Feature Attribution Methods Correctly Attribute Features?
https://github.com/YuqiCui/TSK_BN_UR
공식 코드https://github.com/abdelkadermh/isarcasmeval
공식 코드https://github.com/abidlabs/contrastive
공식 코드https://github.com/abrazinskas/sigir2022-opinion-summarization-tutorial
공식 코드Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
https://github.com/achyudhk/SentiGH
공식 코드Supervised Sentiment Classification with CNNs for Diverse SE Datasets
https://github.com/adaamko/potato
공식 코드https://github.com/adewynter/argumentation-llms
공식 코드"I'd Like to Have an Argument, Please": Argumentative Reasoning in Large Language Models
https://github.com/adlnlp/doc_gcn
공식 코드Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis
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