SadOrHappy1's Stars
Jack24658735/FedLGT
[AAAI 2024] Official Implementation of Language-Guided Transformer for Federated Multi-Label Classification
HaokunChen245/FedDAT
[AAAI 2024] FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
Rebzzel/PicPrompt
Fast, simple and lightweight image viewer.
zdy1988/Zhai.PictureView
A beautiful and practical picture browser...
ChiNoel-osu/FolderThumbnailExplorer
Better desktop image folder preview experience.
HeBianGu/WPF-ImagePlayer
WPF开发的图片浏览和播放控件
ldqk/ImageSearch
基于.NET8的本地硬盘千万级图库以图搜图案例Demo和图片exif信息移除小工具分享
TsingZ0/PFLlib
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.
TsingZ0/FedTGP
AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
TsingZ0/HtFLlib
Configure one file for model heterogeneity. Consistent GPU memory usage for single or multiple clients.
LipingYi/FedGH
FedGH: Heterogeneous Federated Learning with Generalized Global Header (MM'23)
Starlien95/GraphPrompt
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
vanoracai/MixupForGraph
Code for paper "Mixup for Node and Graph Classification", WWW 2021
Leejiaxing/GAMT
Multi-scale Self-attention Mixup for Graph Classification
zhumeiqiBUPT/AM-GCN
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
ahxt/g-mixup
[ICML2022] G-Mixup: Graph Data Augmentation for Graph Classification
FighterLYL/GraphNeuralNetwork
《深入浅出图神经网络:GNN原理解析》配套代码
NLP-LOVE/ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。
Diego999/pyGAT
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
dragen1860/GCN-PyTorch
Graph Convolution Network for PyTorch
fengdu78/Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
fengdu78/deeplearning_ai_books
deeplearning.ai(吴恩达老师的深度学习课程笔记及资源)
scutan90/DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
Code-Deer/KNN-Fall-Detection
基于Mediapipe框架检测人体3D骨架,KNN算法识别人体是否跌倒。