GECCO是一种基于单个MLP和图卷积层的轻量级图像分类器。我们发现，与其他最先进的模型相比，我们的模型可以实现高达16倍的延迟。我们模型的论文可以在https:arxiv.org abs 2402.00564上找到
GECCO is a lightweight image classifier based on single MLP and graph convolutional layers. We find that our model can achieve up to 16x better latency than other state-of-the-art models. The paper for our model can be found at https: arxiv.org abs 2402.00564 (2024-02-27, Python, 0KB, 下载0次)
“传播就是你所需要的：图的表示学习和分类器训练的新框架”的pytorch实现。，
A pytorch implementation of "Propagation is All You Need: A New Framework for Representation Learning and Classifier Training on Graphs"., (2023-08-27, Python, 0KB, 下载0次)
用于使用简单的基于图的...在UMLS元词库知识图和自由文本语料库上联合训练BERT编码器模型的工具包...，
Toolkit for jointly training BERT encoder models on the UMLS metathesaurus knowledge graph and free-text corpora using simple graph-based reasoning tasks alongside masked-language modelling (2023-07-27, Python, 0KB, 下载0次)
GNN编码器音乐推荐模型的对比学习。使用Spotify API进行数据收集。实现实时...
Contrastive Learning With GNN Encoder Music Recommendation Model. Data Collection Using Spotify API. Achieve Real-time inference through architectural adjustment, model optimization and data structure modification (2022-06-06, Python, 19859KB, 下载0次)
图转换器的非官方实现（屏蔽标签预测：半苏的统一消息传递模型...
An unofficial implementation of Graph Transformer (Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification) - IJCAI 2021 (2023-01-30, Python, 291KB, 下载0次)
“一种基于动态图的多模式无线传感器网络数据流异常检测新方法”中所提出方法的测试视频
test video of the proposed method in "A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph (2023-06-05, Python, 28196KB, 下载0次)
Pytorch和Pytorch Geometric中用于图神经网络的多掩码聚合器的正式实现
Official Implementation of Multi-Masked Aggregators for Graph Neural Networks in Pytorch and PyTorch Geometric (2023-03-24, Python, 5291KB, 下载0次)
源代码来自文章“用于联合社区检测和链接预测的模块化感知图自动编码器”（N...
Source code from the article "Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction" (Neural Networks, 2022) (2022-12-06, Python, 11646KB, 下载0次)
一个用于创建图神经网络的模型构建器助手，类似于[https://arxiv.org abs 1806.0...]（https://arxiv.org abs 1806.01261）中描述的网络
A model builder helper for creating graph neural networks akin to the ones described in <https://arxiv.org/abs/1806.01261> and <https://ieeexplore.ieee.org/document/4700287> (2019-04-18, Python, 17KB, 下载0次)
这是IJCAI 2022论文的实现演示[使用...消除深度神经网络的后门触发器...
This is an implementation demo of the IJCAI 2022 paper [Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation](<https://arxiv.org/abs/2204.09975>) in PyTorch. (2022-07-04, Python, 17666KB, 下载0次)