Papers with Code 아카이브
Decision Making
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fabltt/explaineddecisions
공식 코드https://github.com/fabriziocarpi/RLdecoding
공식 코드Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
https://github.com/fabstat/burbank
공식 코드https://github.com/facebookresearch/CoDraw
공식 코드CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
https://github.com/facebookresearch/OccupancyAnticipation
공식 코드Occupancy Anticipation for Efficient Exploration and Navigation
https://github.com/facebookresearch/blr-hac
공식 코드Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration
https://github.com/facebookresearch/codraw-models
공식 코드CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
https://github.com/facebookresearch/dcd
공식 코드https://github.com/facebookresearch/minimax
공식 코드https://github.com/facebookresearch/minirts
공식 코드Hierarchical Decision Making by Generating and Following Natural Language Instructions
https://github.com/facebookresearch/motif
공식 코드Motif: Intrinsic Motivation from Artificial Intelligence Feedback
https://github.com/facebookresearch/mtm
공식 코드Masked Trajectory Models for Prediction, Representation, and Control
https://github.com/facebookresearch/nle
공식 코드https://github.com/facebookresearch/nocturne
공식 코드https://github.com/facebookresearch/oni
공식 코드Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
https://github.com/facebookresearch/pytouch
공식 코드https://github.com/facebookresearch/searchformer
공식 코드Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
https://github.com/facebookresearch/uimnet
공식 코드https://github.com/fairinterpret/fair_images
공식 코드Fairness Explainability using Optimal Transport with Applications in Image Classification
https://github.com/fairwashing/fairwashing
공식 코드https://github.com/familyld/insightfair
공식 코드What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
https://github.com/fangchenliu/maskdp_public
공식 코드Masked Autoencoding for Scalable and Generalizable Decision Making
https://github.com/fangleigit/RetroSub
공식 코드Leveraging Reaction-aware Substructures for Retrosynthesis Analysis
https://github.com/fangshiyuu/codrivingllm
공식 코드https://github.com/fanhanwei/fnn_mfrl_archdse
공식 코드https://github.com/fanyun-sun/InfoGraph
공식 코드Towards Explanation for Unsupervised Graph-Level Representation Learning
https://github.com/farakiko/xai4hep
공식 코드Do graph neural networks learn traditional jet substructure?
https://github.com/farama-foundation/momaland
공식 코드MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
https://github.com/faroukmokhtar/particleflow
공식 코드https://github.com/fatemehN/entangled_representation
공식 코드$β$-Multivariational Autoencoder for Entangled Representation Learning in Video Frames
https://github.com/fau-is/xnap
공식 코드XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
https://github.com/fawazsammani/nlxgpt
공식 코드NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
https://github.com/faysal-md/an-interpretable-deep-learning-approach-for-skin-cancer-categorization-ieee2023
공식 코드An Interpretable Deep Learning Approach for Skin Cancer Categorization
https://github.com/fbiying87/demographic-bias-visualization
공식 코드Towards Explaining Demographic Bias through the Eyes of Face Recognition Models
https://github.com/fduxuan/sdm-attack
공식 코드Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.