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Decision Making

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

https://github.com/pacifinapacific/awesome_sportanalysis_paper

SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer

https://github.com/pairlab/robosuite

Reinforcement and Imitation Learning for Diverse Visuomotor Skills

https://github.com/panmt/mbrl_with_isolated_imaginations

Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models

https://github.com/patrickhart/jaxdl

Relational inductive biases, deep learning, and graph networks

https://github.com/patrikken/fair-dsr/blob/main/src/FAIRDA/fair_da.py

Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach

https://github.com/peanutshawny/lstm-stock-predictor

Twitter mood predicts the stock market

https://github.com/pedrocurvo/govsim

Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents

https://github.com/peiyannn/mm-pde

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution

https://github.com/peteflorence/visuomotor_correspondence

Self-Supervised Correspondence in Visuomotor Policy Learning

https://github.com/pfnet-research/chainer-differentiable-mpc

Differentiable MPC for End-to-end Planning and Control

https://github.com/phixion/phixion

DAWN: Designing Distributed Agents in a Worldwide Network

https://github.com/phunterlau/paper_without_code/tree/main/examples/agentq

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents

https://github.com/pie33000/OpenVLA

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

https://github.com/pihey1995/DistributionalRL

QUOTA: The Quantile Option Architecture for Reinforcement Learning

https://github.com/pimdh/causal-confusion

Causal Confusion in Imitation Learning

https://github.com/pjhool/TF-A2RL-Test

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

https://github.com/pkd667/political-alignment

PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models

https://github.com/pko89403/llm-for-recsys-shadow

MACRec: a Multi-Agent Collaboration Framework for Recommendation

https://github.com/pku-aicare/colacare

ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration

https://github.com/pku-rl/dgn

Graph Convolutional Reinforcement Learning

https://github.com/plaffa/discrete-world-models

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/pnlbwh/CNN-Diffusion-MRIBrain-Segmentation

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/polixir/NeoRL

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

https://github.com/pranz24/pytorch-soft-actor-critic

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

https://github.com/predict-idlab/landmarker

Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization

https://github.com/priyanka-sharma29/Robust-Optimization-using-Machine-Learning-for-building-Uncertainty-Sets

Robust Optimization using Machine Learning for Uncertainty Sets

https://github.com/priyeshshukla/graph_nets_ids576

Relational inductive biases, deep learning, and graph networks

https://github.com/probabilists/zuko

Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows

https://github.com/proroklab/gnn_pathplanning

Graph Neural Networks for Decentralized Multi-Robot Path Planning

https://github.com/ptuls/tabnet-modified

TabNet: Attentive Interpretable Tabular Learning

https://github.com/purdue-m2/auc_fairness_with_noisy_groups

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

https://github.com/purdue-m2/fairness-generalization

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

https://github.com/purdue-m2/robust_fairness_withNoisy_labels

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

https://github.com/pwc-1/Paper-9/tree/main/1/tapas

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

https://github.com/py-econometrics/duckreg

You Only Compress Once: Optimal Data Compression for Estimating Linear Models

https://github.com/pykao/Modified-3D-UNet-Pytorch

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

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