https://github.com/danilonumeroso/MEG
MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks
Papers with Code 아카이브
Explainable Artificial Intelligence (XAI)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Explainable Artificial Intelligence
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dbash/zerowaste
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/dbvis-ukon/explainer
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
https://github.com/ftorres11/saliencysense
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/gesina/bg_randomized_loce
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces
https://github.com/grobruegge/hu-mcd
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
https://github.com/h2oai/xai_guidelines
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
https://github.com/hammerlabml/diversesemifactualsreject
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/jettify/ibreakdown
https://github.com/joakimedin/medical-coding-reproducibility
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records
https://github.com/johkruse/XAI-for-grid-frequency-stability
Revealing drivers and risks for power grid frequency stability with explainable AI
https://github.com/jphall663/hc_ml
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
https://github.com/jphall663/interpretable_machine_learning_with_python
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
https://github.com/manmeet3591/python_class
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset
https://github.com/mattemartini/explainable-and-trustworthy-ai-project
ferret: a Framework for Benchmarking Explainers on Transformers
https://github.com/myurasov/RISE
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/nealgravindra/self-supervsed_edge_feats
https://github.com/nickkantack/Hanabi-Cyclone
Instructive artificial intelligence (AI) for human training, assistance, and explainability
https://github.com/openvinotoolkit/openvino_xai
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/palatos/RISE_tf
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/pwc-1/Paper-10/tree/main/t5
https://github.com/sebastian-lapuschkin/explaining-deep-clinical-gait-classification
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
https://github.com/sebastian-lapuschkin/interpretable-deep-gait-injury
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
https://github.com/sebastian-lapuschkin/lrp_toolbox
Towards Best Practice in Explaining Neural Network Decisions with LRP
https://github.com/shenchenyi/SpeakerData
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
https://github.com/sooeun67/xai
https://github.com/tmehari/feature_importance
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
https://github.com/tristangomez44/metrics-saliency-maps
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/vin-nag/XAI_GAN
xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems
https://github.com/visinf/funnybirds-framework
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods
https://github.com/vlue-c/PyTorch-Explanations
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/vlue-c/Visual-Explanation-Methods-PyTorch
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/wickstrom/relax
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/yiskw713/RISE
RISE: Randomized Input Sampling for Explanation of Black-box Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.