https://github.com/jacobgil/pytorch-explain-black-box
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Papers with Code 아카이브
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/jacobgil/pytorch-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/jakeyap/MultiTaskLearning
Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations
https://github.com/janhavi-giri/Explainable_ML_Classifier_LIME
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jdb78/pytorch-forecasting
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/jemilc/shap
https://github.com/jeongukjae/smaller-labse
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/jgreitemann/svm-order-params
Probing hidden spin order with interpretable machine learning
https://github.com/jhcircle/less-is-more
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
https://github.com/jiamingkong/infoxlm_paddle
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/jmvuilla/classification
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/jordan-bird/synthetic-fruit-image-generator
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/kaiyuanmifen/A-machine-learning-methodology-for-real-time-forecasting-of-the-2019-2020-COVID-19-outbreak-using-In
https://github.com/kalelpark/Mixture-of-Granularity-Specific-Experts-for-Fine-Grained-Categorization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/karandesaiii/CheXNet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/kazuto1011/grad-cam-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/kherud/neural-additive-models-pt
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/kingkonk81/cologne
https://github.com/kjczarne/eager_gradcam_tf
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/kmkwon94/ainize-laser
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/koren-v/Interpret
https://github.com/labouz/xai-pres-1
Interpretable Explanations of Black Boxes by Meaningful Perturbation
https://github.com/laura-rieger/deep-explanation-penalization
Interpretable machine learning: definitions, methods, and applications
https://github.com/linkedin/fasttreeshap
https://github.com/liuyanguu/SHAPforxgboost
https://github.com/lpcan/micl
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/luulinh90s/CFD
Explaining How Deep Neural Networks Forget by Deep Visualization
https://github.com/mankadronit/60DaysofUdacity-Challenge
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text
https://github.com/marcotcr/lime
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/marnifora/magisterka
https://github.com/mathworks/Grad-CAM-for-AlexNet-to-explain-the-reason-of-classification
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/matteo-rizzo/explainable-fruit-ripeness-classification
https://github.com/mattsherar/Temporal_Fusion_Transform
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/mavarick/tft
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/meliketoy/gradcam.pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.