与Seq2Seq类似,该模型包括两个部分,一个Graph编码器与一个Sequence解码器。Graph编码器将输入的图映射为一个图的表示以及一系列节点表示;而Sequence解码器则基于这些表示,生成相应的目标序列。通过在bAbI任务、最短路径任务以及自然语言生成任务上的实验,本文证明了Graph2Seq模型可以被有效地用于从图到序列的任务。 总...
To address this challenge, we propose a joint graph and sequence representation learning model for drug response prediction, called DGSDRP. We use convolutional neural networks (CNN) to obtain local chemical context information from the drug sequences and a fusion module based on CNN and Bi-LSTM...
GRAPH2SEQ: GRAPH TO SEQUENCE LEARNING WITH ATTENTION-BASED NEURAL NETWORKS,程序员大本营,技术文章内容聚合第一站。
《Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks》阅读笔记 作者:Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, and Vadim Sheinin 来源:arXiv 链接: link研究机构:IBM Research, IBM T. J. Watson Research Center; Institute of Computer S… 孙建东 Graph Embedding:从DeepW...
Celebrated \emph{Sequence to Sequence learning (Seq2Seq)} and its fruitful variants are powerful models to achieve excellent performance on the tasks that map sequences to sequences. However, these are many machine learning tasks with inputs naturally represented in a form of graphs, which impose...
and information-theoretic measures are used to analyse the online learning process. By constructing a numerical sequence pattern using the MNIST database, we then develop and implement a novel sequence memory task that demonstrates the NWN’s ability to generate spatiotemporal memory patterns in a si...
论文解读:Graph Transformer for Graph-to-Sequence Learning 图神经网络在深度学习领域内得到十分广泛的应用,其可以对拓扑结构的数据进行表征。现阶段传统的以GNN及其相关变体在进行表征时普遍做法是将结点多跳范围内的邻居结点通过平均或加权等方式进行聚合,但这类方式存在一些不足之处,本篇文章提出的Graph Tran...
3.1 Graph Convolutional Networks 给定一个图 ,其中 和 分别表示图中的节点集和边集。每个节点 有一个特征向量 。邻接矩阵 用于表示图中的连接。图神经网络 (GNNs) 从图结构和节点特征中学习节点和图的特征表示。大多数现有的图神经网络遵循一种邻域聚合学习策略,即每个节点迭代地从其邻域聚合特征并更新其特征。特...
Tracing a student's knowledge state is critical for teaching and learning. Knowledge tracing aims to accurately predict student performance by analyzing hi... Z Chen,Z Shan,Y Zeng - User modeling and user-adapted interaction 被引量: 0发表: 2024年 Temporal Graph Memory Networks For Knowledge Tra...
Graph neural networkgraph to sequence learningnetwork alignmentNetwork alignment aims at detecting the corresponding entities across multiple networks, which is an essential basis for the fusion and analysis of multiple network information. Moreover, embedding-based network alignment has gradually become one...