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://gitlab.physik.uni-muenchen.de/tk-svm/tksvm-op
공식 코드Probing hidden spin order with interpretable machine learning
https://codigos.ufsc.br/lapix/Sketch2AIA
https://github.com/11301858/xaisuite
https://github.com/AWarno/CancerOptimization
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/AhnYoungBin/Grad_cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlanYangYi/GayPhotoStyleRecongnizer
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlanYangYi/PhotoStyleRecongnizer
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AldrichCabrera/gradCam-smallvgg
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AlejandroAttento/Pytorch-Captum
https://github.com/AlexIoannides/lime-interpretable-ml
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/AlexSzen/wwf_shark_id
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AmrMKayid/nam
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/Ankush96/grad-cam.tensorflow
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/AstraZeneca/chemicalx
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
https://github.com/BunsenFeng/news_stance_detection
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Cherrydomini/Effects-of-feature-dropping-on-COMPAS-with-the-influence-of-LIME-
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/Cloud-CV/Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Deepak3994/Grad-Cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Deepak3994/grad-cam-editted
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Eljefemasao/Customed-Object-Detector
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Eljefemasao/MarkNet-ObjectDetector
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Fragjacker/Pointcloud-grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/FrancescoSaverioZuppichini/A-journey-into-Convolutional-Neural-Network-visualization-
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/GISH123/Cathay-Holdings-CIP-Projects-for-Interpretable-Machine-Learning
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/HMProenca/RobustSubgroupDiscovery
Discovering outstanding subgroup lists for numeric targets using MDL
https://github.com/HMProenca/robust-rules-for-prediction-and-description
Discovering outstanding subgroup lists for numeric targets using MDL
https://github.com/Habush/nam_jax
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/HarryHe11/CSD
https://github.com/Hydragon516/Grad-CAM-pytorch
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/IKKIM00/multi-horizon-forecasting-comparison-between-TFT-and-DL-methods
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/IKKIM00/stock_and_pm2.5_prediction_using_TFT
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/IS2AI/Chest-X-ray-module
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Imageomics/Prompt_CAM
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
https://github.com/Jara555/CNN-for-AD-classification
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.