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/HaiDang9719/MatLabProj

Decision support from financial disclosures with deep neural networks and transfer learning

https://github.com/Haichao-Zhang/IRL

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

https://github.com/HanjieChen/HEDGE

Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection

https://github.com/HarryLui98/DMPEL

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

https://github.com/HoganZhang/pytorch-vsumm-reinforce

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

https://github.com/Homagn/MultiAgentRL

A perspective on multi-agent communication for information fusion

https://github.com/HumanCompatibleAI/airl

imitation: Clean Imitation Learning Implementations

https://github.com/HumanCompatibleAI/deep-rlsp

Generative Adversarial Imitation Learning

https://github.com/Humboldt-WI/supervised_randomization

Affordable Uplift: Supervised Randomization in Controlled Experiments

https://github.com/IMAC-projects/mesh-deformation

Modeling 3D Shapes by Reinforcement Learning

https://github.com/INF554MachineLearning/GameAI

An Introduction to Deep Reinforcement Learning

https://github.com/IRVLab/UGAN

Enhancing Underwater Imagery using Generative Adversarial Networks

https://github.com/Ibotta/mr_uplift

Uplift Modeling with Multiple Treatments and General Response Types

https://github.com/IirisSundin/active-learning-for-decision-making

Active Learning for Decision-Making from Imbalanced Observational Data

https://github.com/IlyaGusev/summarus

Deep Reinforcement Learning For Sequence to Sequence Models

https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM

A General Language Assistant as a Laboratory for Alignment

https://github.com/Ipsedo/EvoMotion

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

https://github.com/Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

https://github.com/JamesGlare/Neural-Net-LabView-DLL

Deep Bayesian Inversion

https://github.com/JiazhengChai/synergyDRL

Soft Actor-Critic Algorithms and Applications

https://github.com/JohnKurian/tabnet

TabNet: Attentive Interpretable Tabular Learning

https://github.com/Josh00-Lu/DiffusionVeteran

CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making

https://github.com/Jupaoqq/Jupaoqq_LaRL

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

https://github.com/Jupetus/ExplainableAI

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

https://github.com/KAIST-AILab/deeprl_practice_colab

Generative Adversarial Imitation Learning

https://github.com/Kaixhin/EC

Model-Free Episodic Control

https://github.com/KaiyangZhou/pytorch-vsumm-reinforce

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

https://github.com/Kchu/DeepRL_CK

QUOTA: The Quantile Option Architecture for Reinforcement Learning

https://github.com/Khrylx/PyTorch-RL

Generative Adversarial Imitation Learning

https://github.com/Kikumu/Reinforcement-Learning-repo

Reinforcement Learning

https://github.com/Kin-Zhang/mmfn

Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

https://github.com/KrishnaRJ422/Explainability_Bias_Fairness-in-AI

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved

https://github.com/KrishnaswamyLab/ImageFlowNet

ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images

https://github.com/KrishnaswamyLab/logicml

Making Logic Learnable With Neural Networks

https://github.com/KrissHs/KIT19

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

https://github.com/Kryvkodenis/TabNet

TabNet: Attentive Interpretable Tabular Learning

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