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Decision Making

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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/yifan-fanyi/Pixelhop

PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification

https://github.com/yifan-fanyi/myMCL-Realization

PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification

https://github.com/yifan123/arxiv_spider

False Correlation Reduction for Offline Reinforcement Learning

https://github.com/yikuide/graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/yimingpeng/sac-master

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

https://github.com/ying531/conformal-selection

Selection by Prediction with Conformal p-values

https://github.com/yining043/SAC-discrete

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

https://github.com/yinyueqin/denserewardrlhf-ppo

Preference-grounded Token-level Guidance for Language Model Fine-tuning

https://github.com/yishuwei2019/DL-based-dynamic-risk-prediciton

Deep Learning for Patient-Specific Kidney Graft Survival Analysis

https://github.com/yiskw713/scorecam

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

https://github.com/ymlwww/rlproject

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

https://github.com/yoojungsun0/Psych239

Model-Free Episodic Control

https://github.com/young-geng/cql

Soft Actor-Critic Algorithms and Applications

https://github.com/young-geng/jaxcql

Soft Actor-Critic Algorithms and Applications

https://github.com/yschroecker/universal_value_density_estimation

Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning

https://github.com/yubaoliu/caffe-segnet

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

https://github.com/yuchenlin/LLM-Blender

WebGPT: Browser-assisted question-answering with human feedback

https://github.com/yuchenxia/aasbyllm

LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins

https://github.com/yufeiwang63/L2D-learning-to-discretize

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

https://github.com/yufeiwu1011/Robust-Amortized-Bayesian-Inference

A Deep Learning Method for Comparing Bayesian Hierarchical Models

https://github.com/yuweisunn/homogeneous-learning

The Consciousness Prior

https://github.com/ywchao/merel-mocap-gail

Learning human behaviors from motion capture by adversarial imitation

https://github.com/zeroxleo/ml-agent

MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

https://github.com/zhanglab-aim/cancer-net

Relational inductive biases, deep learning, and graph networks

https://github.com/zhaoolee/garss

On Large Language Models in National Security Applications

https://github.com/zhengyinan-air/diffusion-planner

Diffusion-Based Planning for Autonomous Driving with Flexible Guidance

https://github.com/zhoujuncc1/torch_ihmehimmeli

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

https://github.com/zhucheng725/MobileNetv1_Segnet_keras_tensorrt

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

https://github.com/zihanwang314/ragen

RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

https://github.com/zivaharoni/capacity-rl

Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning

https://gitlab.com/dlr-dw/fastcav

Using Causal Analysis for Conceptual Deep Learning Explanation

https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

https://huggingface.co/datasets/Open-Orca/OpenOrca

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

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