susan1314 Goto Github PK
Type: User
Type: User
This thesis proposes the enhancing of state estimation and constraint detection of the transmission and distribution grids through the use of machine learning methods. This contribution is relevant once the current methodology suffers from inaccurate or lack of system information in order to perform numerical methods. Taking into consideration the several machine learning techniques presented in the literature, a range of models will be tested in the framing of both classification and regression problems, with the aim of achieving accurate predictions.
This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].
This repositories contains the source code to carry out benchmarking of position aware graph neural networks
Whole building non-residential hourly energy meter data from the Great Energy Predictor III competition
船长关于机器学习、计算机视觉和工程技术的总结和分享
科研工作专用ChatGPT拓展,特别优化学术Paper润色体验,支持自定义快捷按钮,支持markdown表格显示,Tex公式双显示,代码显示功能完善,新增本地Python工程剖析功能/自我剖析功能
Use ChatGPT to summary the Arxiv papers.
Pytorch implementation of the paper "Circle Loss: A Unified Perspective of Pair Similarity Optimization"
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks
CS224W Stanford Winter 2021 Homework solutions
[ICCV2021] Official code for "Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition"
Materials for a short course on convex optimization.
A Python-embedded modeling language for convex optimization problems.
Implementations of algorithms from the Q-learning family. Implementations inlcude: DQN, DDQN, Dueling DQN, PER+DQN, Noisy DQN, C51
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
A PyTorch implementation of DGCNN based on AAAI 2018 paper "An End-to-End Deep Learning Architecture for Graph Classification"
A simulation framework for topology identification and model parameter estimation in power distribution grids: https://ieeexplore.ieee.org/document/8601410
A collection of distribution power grid data source
Algorithms for calibrating power grid distribution system models
雨花阁发布页
Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch
DyHATR (ECML-PKDD 2020)
Code for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
An open source python library for automated feature engineering
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.