Papers with Code 아카이브
Decision Making
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yinan-xia/pdf
공식 코드https://github.com/ying-wen/gr2
공식 코드Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
https://github.com/ying531/selcf_paper
공식 코드https://github.com/yingqichao/fnd-bootstrap
공식 코드Bootstrapping Multi-view Representations for Fake News Detection
https://github.com/yinhanhe123/new_LLM4GNNExplanation
공식 코드https://github.com/yinxusen/learning_to_generalize
공식 코드https://github.com/yiqingxyq/doclens
공식 코드DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
https://github.com/yiwei-liao/isac
공식 코드Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework
https://github.com/yiweijiang2015/cookdial
공식 코드CookDial: A dataset for task-oriented dialogs grounded in procedural documents
https://github.com/yixinchen-ai/mpum
공식 코드Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation
https://github.com/yixinghuang/chatgpt-benchmark-on-radiation-oncology
공식 코드https://github.com/yiyhuang3/cate_estimator_selection
공식 코드Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
https://github.com/yizhengw/optimizingcarbonstorage
공식 코드https://github.com/yjchoe/comparingabstainingclassifiers
공식 코드https://github.com/yjliu7/neural-adaptive-empirical-bayes
공식 코드https://github.com/ynulihao/LLM_vs_Human_Decision_Making
공식 코드Large Language Models are Near-Optimal Decision-Makers with a Non-Human Learning Behavior
https://github.com/yobibyte/atarigrandchallenge
공식 코드https://github.com/yohanjo/multiuser_multiwoz
공식 코드Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
https://github.com/yolandalalala/gnninterpreter
공식 코드GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
https://github.com/yotamitai/coviz
공식 코드Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
https://github.com/yottaxx/et5
공식 코드ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
https://github.com/youlei202/research-weighted-sum-rate
공식 코드Weighted Sum-Rate Maximization With Causal Inference for Latent Interference Estimation
https://github.com/youngdubbydu/llm-agent-optimization
공식 코드A Survey on the Optimization of Large Language Model-based Agents
https://github.com/younghuman/llmagent
공식 코드Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions
https://github.com/yplarrywang/beyond-agreement-aes-2024
공식 코드https://github.com/ysaikai/genweather
공식 코드https://github.com/ysmei97/mac-po
공식 코드MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
https://github.com/ysymyth/ReAct
공식 코드https://github.com/ysymyth/awesome-language-agents
공식 코드https://github.com/ysymyth/tree-of-thought-llm
공식 코드Tree of Thoughts: Deliberate Problem Solving with Large Language Models
https://github.com/ythuang02/AppealCase
공식 코드AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
https://github.com/ytongxie/medical-vision-and-language-tasks-and-methodologies-a-survey
공식 코드A Survey of Medical Vision-and-Language Applications and Their Techniques
https://github.com/yu-group/imodels-experiments
공식 코드https://github.com/yuancheng-xu/elbert
공식 코드https://github.com/yuanyunshuang/cosense3d
공식 코드CoSense3D: an Agent-based Efficient Learning Framework for Collective Perception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.