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/facebookresearch/vizseq
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/fitushar/3D-Grad-CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/fortuinlab/la-nam
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/frgfm/torch-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ftorres11/saliencysense
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/gablabc/shap
https://github.com/galdeia/iirsbenchmark
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/garygsw/smooth-taylor
https://github.com/gcskoenig/fippy
https://github.com/gcskoenig/rfi
https://github.com/ggaggu/Diprunning_study
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/google-research/google-research/tree/master/neural_additive_models
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/google-research/google-research/tree/master/protoattend
https://github.com/google-research/google-research/tree/master/tft
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/greatwhiz/tft_tf2
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/gshashank84/CheXNet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/gujingxiao/BaiduStar2020-Traffic-Sign-Detection-And-Pair-Competition-Solution
https://github.com/hannamw/eap-ig
https://github.com/heidelberg-nlp/amr-argument-sim
Classification and Clustering of Arguments with Contextualized Word Embeddings
https://github.com/henghamao/tft
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
https://github.com/hftsoi/symbolfit
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
https://github.com/hieu2906090/deep-learning-in-js
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/hs2k/pytorch-smoothgrad
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/hysts/pytorch_D-RISE
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/iamollas/LionLearn/tree/master/LioNets_V2
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information
https://github.com/iancovert/shapley-regression
https://github.com/ibm/kpa_2021_shared_task
Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis
https://github.com/imagearg/imagearg-shared-task
Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining
https://github.com/imamathcat/LASER_Dependencies
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/isaaccasm/GradCAM-keras
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ismailuddin/gradcam-tensorflow-2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/itanvir/gradcam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/jackaduma/NLP4CyberSecurity
https://github.com/jacobgil/keras-grad-cam
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.