https://github.com/poloclub/FairVis
공식 코드FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Papers with Code 아카이브
Interpretable Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/potamides/unsupervised-metrics
공식 코드USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation
https://github.com/princeton-nlp/transformerprograms
공식 코드https://github.com/publisher-promptcd/promptcd
공식 코드Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis
https://github.com/pysentimiento/pysentimiento
공식 코드pysentimiento: A Python Toolkit for Opinion Mining and Social NLP tasks
https://github.com/ragerri/antidote-projections
공식 코드https://github.com/rajesh-lab/realx
공식 코드https://github.com/rajpurkarlab/lymphoml
공식 코드https://github.com/ramprs/grad-cam
공식 코드Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/raruidol/budget-am
공식 코드https://github.com/rbun013/Rugby-Sequence-Data
공식 코드https://github.com/remilyoucef/sca-miner
공식 코드Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets
https://github.com/remilyoucef/sd-4sql
공식 코드"What makes my queries slow?": Subgroup Discovery for SQL Workload Analysis
https://github.com/rit-git/Snippext_public
공식 코드Snippext: Semi-supervised Opinion Mining with Augmented Data
https://github.com/roycmeghna/i-hope
공식 코드Predicting and Understanding College Student Mental Health with Interpretable Machine Learning
https://github.com/roznn/dec
공식 코드https://github.com/ruthcfong/perturb_explanations
공식 코드Interpretable Explanations of Black Boxes by Meaningful Perturbation
https://github.com/ryang1119/Online-Opinion-Mining
공식 코드Can Large Language Models be Effective Online Opinion Miners?
https://github.com/ryo-asashi/midr
공식 코드midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
https://github.com/salesforce/omnixai
공식 코드https://github.com/saranya8989/Causal_ML
공식 코드Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
https://github.com/seed-vt/tracefl
공식 코드TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
https://github.com/selbosh/p-hacking
공식 코드https://github.com/selfexplainml/piml-toolbox
공식 코드PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics
https://github.com/serenayj/ABCD-ACL2021
공식 코드ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
https://github.com/serenayj/DeSSE
공식 코드ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
https://github.com/shaopengyang/isg-cd
공식 코드https://github.com/shef-aire/stoiciml
공식 코드Classifying the Stoichiometry of Virus-like Particles with Interpretable Machine Learning
https://github.com/shinkungoo/symboliccdm
공식 코드Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems
https://github.com/shouryaverma/interpretable_ml_ecg
공식 코드Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data
https://github.com/shyyhs/CourseraParallelCorpusMining
공식 코드Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation
https://github.com/simsal0r/mixture-of-decision-trees
공식 코드Mixture of Decision Trees for Interpretable Machine Learning
https://github.com/skasapis/sep_pred_smarp-sharp
공식 코드Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning
https://github.com/slds-lmu/paper_2021_xautoml
공식 코드Explaining Hyperparameter Optimization via Partial Dependence Plots
https://github.com/sonialagunac/beyond-cbm
공식 코드Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
https://github.com/sophhan/imlsa_2024
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.