yuanpf123

yuanpf123's Stars

• iclavera/learning_to_adapt

  Learning to Adapt in Dynamic, Real-World Environment through Meta-Reinforcement Learning

  Language:Python20749

• omerbsezer/Reinforcement_learning_tutorial_with_demo

  Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

  Language:Jupyter Notebook722170

• Farama-Foundation/Metaworld

  Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning

  Language:Python1.2k269

• PaddlePaddle/MetaGym

  Collection of Reinforcement Learning / Meta Reinforcement Learning Environments.

  Language:Python27659

• fanrena/DPT

  The official implementation of paper Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model. If you find our code or paper useful, please give us a citation.

  Language:Python414

• XY-boy/TTST

  [IEEE TIP 2024] TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution

  Language:Python854

• lshiwjx/2s-AGCN

  Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

  Language:Python659177

• wjn1996/scrapy_for_zh_wiki

  基于scrapy的层次优先队列方法爬取中文维基百科，并自动抽取结构和半结构数据

  Language:Python13219

• XLearning-SCU/2024-TIP-CREAM

  PyTorch implementation for Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and Mining (TIP 2024)

  Language:Python121

• Junyoungpark/PGNN

  An official 're'-implementation of Physics-induced graph neural network: An application to wind-farm power estimation (PGNN).

  Language:Python262

• tanushBee/Power-flow-analysis-via-typed-graph-neural-networks

  This repository contains the code for Power flow analysis via typed graph neural networks

  Language:Python4

• CanBayraktaroglu/OPFGNN

  Master Thesis: Optimal Power in Power Systems using Graph Neural Networks

  Language:Jupyter Notebook6

• hyperion0706/MatIdentification

  Electrical Engineering: A tool to identify topology and parameters of a distribution network in Matlab.

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• xnuohz/ARMA-dgl

  A DGL implementation of "Graph Neural Networks with convolutional ARMA filters". (PAMI 2021)

  Language:Python182

• Alexisaglar/GAT_power_system

  Graph Attention Network utilised to predict the voltage and voltage angles of a given N bus network.

  Language:Python1

• shiftlin89/PowerSystem_Viewer

  Power system topology visualization, autolayout the graph and show the data for the system

  Language:MATLAB2

• akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network

  This GitHub repository provides an implementation of the paper "MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network" . MAGNET is a state-of-the-art approach for multi-label text classification, leveraging the power of graph neural networks (GNNs) and attention mechanisms.

  Language:Jupyter Notebook13

• power-grid-lib/pglib-opf

  Benchmarks for the Optimal Power Flow Problem

  Language:MATLAB29883

• zyh1996saa/Power-Flow-Calculation-Based-on-a-Physics-Informed-Graph-Neural-Network

  This is an example in the IEEE 39-bus system to illustrate how the algorithm proposed in the paper "Cascading Failure Analysis Based on a Physics-Informed Graph Neural Network" is implemented.

  Language:Python113

• weihua916/powerful-gnns

  How Powerful are Graph Neural Networks?

  Language:Python1.2k223

• mukhlishga/gnn-powerflow

  Graph Neural Network application in predicting AC Power Flow calculation. Developed with Pytorch Geometric framework. My Master Thesis at Eindhoven University of Technology

  Language:Jupyter Notebook6515

• JonasBergHansen/Power-Flow-Balancing-with-Decentralized-Graph-Neural-Networks

  Repository code for Power Flow Balancing with Decentralized Graph Neural Networks

  Language:Python163

• BUAABIGSCity/KDDCUP2022

  [KDD CUP 2022] 11th place solution of Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022

  Language:Python5116

• PaddlePaddle/PGL

  Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

  Language:Python1.6k311

• cnedwards/text2mol

  Repository for Text2Mol: Cross-Modal Molecular Retrieval with Natural Language Queries

  Language:Component Pascal347

• RyanWangZf/BioBridge

  ICLR'24 | BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs

  Language:Jupyter Notebook598

• cshizhe/hgr_v2t

  Code accompanying the paper "Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning".

  Language:Python20921

• PharMolix/OpenBioMed

  Language:Python68176

• m2man/LGSGM

  Language:Jupyter Notebook307

• LivXue/GNN4CMR

  PyTorch implementation of the AAAI-21 paper "Dual Adversarial Label-aware Graph Neural Networks for Cross-modal Retrieval" and the TPAMI-22 paper "Integrating Multi-Label Contrastive Learning with Dual Adversarial Graph Neural Networks for Cross-Modal Retrieval".

  Language:Python333