Papers with Code 아카이브

Decision Making

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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/navganti/SegNet

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/ncbi-nlp/BioSentVec

MedSTS: A Resource for Clinical Semantic Textual Similarity

https://github.com/ncbi-nlp/BioWordVec

MedSTS: A Resource for Clinical Semantic Textual Similarity

https://github.com/ndrwmlnk/critic-guided-segmentation-of-rewarding-objects-in-first-person-views

Critic Guided Segmentation of Rewarding Objects in First-Person Views

https://github.com/neda60/video-summarization-using-FCSN

Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

https://github.com/negrinho/beam_learn_supertagging

Learning Beam Search Policies via Imitation Learning

https://github.com/neuroailab/whisker_model

Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System

https://github.com/nickfrosst/neural_additive_models

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/nicopao/CGM_prediction_data

MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas

https://github.com/nikhilbarhate99/Hierarchical-Actor-Critic-HAC-PyTorch

Learning Multi-Level Hierarchies with Hindsight

https://github.com/nikonkate/DQBirds

Deep Q-Network for Angry Birds

https://github.com/nine09/NLP-

An Introduction to Deep Reinforcement Learning

https://github.com/nine09/NLP-Syllabus

An Introduction to Deep Reinforcement Learning

https://github.com/nishanthdikkala/postprocessing-deferrals

From Soft Classifiers to Hard Decisions: How fair can we be?

https://github.com/nkapetanas/dbpal

An End-to-end Neural Natural Language Interface for Databases

https://github.com/nlpodyssey/spago

TabNet: Attentive Interpretable Tabular Learning

https://github.com/nm512/dreamerv3-torch

Mastering Diverse Domains through World Models

https://github.com/noahshinn/reflexion

Reflexion: Language Agents with Verbal Reinforcement Learning

https://github.com/nrel/m2p

Relational inductive biases, deep learning, and graph networks

https://github.com/nshaud/DeepNetsForEO

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/nvlabs/mimicgen

MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

https://github.com/nvlabs/mimicgen_environments

MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

https://github.com/nvlabs/protomotions

AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control

https://github.com/obster991/RL-model-based-planning-from-scratch

Learning model-based planning from scratch

https://github.com/oliversavolainen/axiomatic-ir-reproduce

Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models

https://github.com/oootttyyy/webarena

Hierarchical Prompting Assists Large Language Model on Web Navigation

https://github.com/openbmb/agentverse

Cognitive Architectures for Language Agents

https://github.com/openclimatefix/skillful_nowcasting

Skillful Precipitation Nowcasting using Deep Generative Models of Radar

https://github.com/opendilab/DI-engine

Deep Q-learning from Demonstrations

https://github.com/opendilab/DI-engine/blob/main/ding/entry/serial_entry_sqil.py

SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

https://github.com/opendilab/DI-engine/blob/main/ding/policy/sac.py

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

https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/gail_irl_model.py

Generative Adversarial Imitation Learning

https://github.com/openvla/openvla

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

https://github.com/oxquantum-repo/drl_for_quantum_measurement

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

https://github.com/oxwhirl/wqmix

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/p10rahulm/your-static-website

A generalized Bayesian framework for the analysis of subscription based businesses

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