![twitter logo](https://raw.githubusercontent.com/github/explore/80688e429a7d4ef2fca1e82350fe8e3517d3494d/topics/twitter/twitter.png)
-
University of Warwick
- Hangzhou, China|Coventry, UK
-
14:12
(UTC +01:00) - https://qianyxxx.github.io
- @YanQian1201
Highlights
- Pro
Block or Report
Block or report qianyxxx
Contact GitHub support about this user’s behavior. Learn more about reporting abuse.
Report abuseLists (1)
Sort Name ascending (A-Z)
Language
Sort by: Recently starred
Starred repositories
[ICML 2024 spotlight] This repository contains the implementation details for the paper "Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Min…
An easy-to-use federated learning platform
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
MambaOut: Do We Really Need Mamba for Vision?
Benchmark of federated learning. Dedicated to the community. 🤗
deep learning for image processing including classification and object-detection etc.
This is code of book "Learn Deep Learning with PyTorch"
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
Official Code Repository for the paper - Personalized Subgraph Federated Learning (ICML 2023)
links to conference publications in graph-based deep learning
AAAI 2023 accepted paper, FedALA: Adaptive Local Aggregation for Personalized Federated Learning
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
Handy PyTorch implementation of Federated Learning (for your painless research)
论文XMind笔记生成工具,将论文pdf通过ChatGPT转换为带有图片和公式的简要XMind笔记,提高论文阅读效率。
Use ChatGPT to summarize the arXiv papers. 全流程加速科研,利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复
[AAAI'23] Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Collection of advice for prospective and current PhD students
[NeurIPS 2019 FL workshop] Federated Learning with Local and Global Representations
A PyTorch Implementation of Federated Learning http:https://doi.org/10.5281/zenodo.4321561