Papers with Code 아카이브

Decision Making

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/shi-ang/survivaleval

Countdown Regression: Sharp and Calibrated Survival Predictions

https://github.com/shiguang-guo/open-grounded-planning

Open Grounded Planning: Challenges and Benchmark Construction

https://github.com/shin-eunsu/GTA_Autodriving_LogitechG29

End-to-end Driving via Conditional Imitation Learning

https://github.com/sholtodouglas/pointMass

Learning Multi-Level Hierarchies with Hindsight

https://github.com/shunchan0677/deepware

End-to-end Driving via Conditional Imitation Learning

https://github.com/si-qi/protoattend

ProtoAttend: Attention-Based Prototypical Learning

https://github.com/sidsvash26/iterx

Iterative Document-level Information Extraction via Imitation Learning

https://github.com/sierra-research/tau-bench

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/simsal0r/mixture-of-decision-trees

MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

https://github.com/sjoshi804/CS-267A-Final-Project

Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review

https://github.com/sjtuzq/Cycle_Dynamics

Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency

https://github.com/skyworkai/skyreels-v2

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

https://github.com/slds-lmu/paper_2024_rpid

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

https://github.com/smlab-niser/23retinald

Multi-Label Retinal Disease Classification using Transformers

https://github.com/snasiriany/leap

Planning with Goal-Conditioned Policies

https://github.com/snisher/Spiking-Neural-Network

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation

https://github.com/snoop2head/ehrxqa-2024

EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

https://github.com/sobieskibj/rcsb

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

https://github.com/soccernet/sn-mvfoul

VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views

https://github.com/somnathhazra/uncertainties_marl

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/sourabhdattawad/TabNet

TabNet: Attentive Interpretable Tabular Learning

https://github.com/spaceml-org/RaVAEn

Fast model inference and training on-board of Satellites

https://github.com/spring-epfl/disparate-vulnerability

On the Privacy Risks of Algorithmic Fairness

https://github.com/spykard/Conditional-Preference-Networks

CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements

https://github.com/ssahn0215/genetic_expert_guided_learning

Guiding Deep Molecular Optimization with Genetic Exploration

https://github.com/ssedai026/uncertainty-segmentation

Analyzing Epistemic and Aleatoric Uncertainty for Drusen Segmentation in Optical Coherence Tomography Images

https://github.com/stanford-iliad/openvla-mini

OpenVLA: An Open-Source Vision-Language-Action Model

https://github.com/stefanknegt/Probabilistic-Unet-Pytorch

A Probabilistic U-Net for Segmentation of Ambiguous Images

https://github.com/stefanknegt/probabilistic_unet_pytorch

A Probabilistic U-Net for Segmentation of Ambiguous Images

https://github.com/stevenkleinegesse/seqbed

Sequential Bayesian Experimental Design for Implicit Models via Mutual Information

https://github.com/stevezhang1990/dma_rl

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

https://github.com/sudh1976/ECE510_DeepLearning_Project

An Alternative Softmax Operator for Reinforcement Learning

https://github.com/sumedh7/RoboCLIP

RoboCLIP: One Demonstration is Enough to Learn Robot Policies

https://github.com/sunfex/weighted-sac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/sunjing1102628/DOMAC

Decision-making with Speculative Opponent Models

https://github.com/sunritc/FLIPHAT

FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.