In this chapter, based on GNN, we propose a multi-channel graph neural network framework to efficiently locate information source. Different from previous methods, we turn this task into a learning problem and design two channels of feature inputs as a solution. Specifically, node channel ...
2 Related Work 随着面向方面的情感分析的蓬勃发展,目前的研究大致可分为三类:基于注意的神经模型(Attention-Based Neural Networks)、基于句法规则的神经网络(Syntactic-Based Recurrent Neural Networks)和基于句法的图神经网络(Syntactic-Based Graph Neural Networks)。我们的工作是基于这些最近的大量努力。 基于语法的图...
MULTI-CHANNEL SPEECH ENHANCEMENT USING GRAPH NEURAL NETWORKS 文献翻译 来自于脸书实验室的一篇文章,将图神经网络用在了多通道语音增强上面,思路比较新奇,下面可以通篇看一下翻译的中文,有哪些值得我们去借鉴的思想。 摘要 多通道语音增强旨在使用从多个麦克风捕获的信号从有噪声的混合信号中提取干净的语音。最近提出的...
Writers: Panagiotis Tzirakis, Anurag Kumar, Jacob Donley PDF:Multi-Channel Speech Enhancement Using Graph Neural Networks Abstract Multi-channel speech enhancement aims to extract clean speech from a noisy mixture using signals captured from multiple microphones. Recently proposed methods tackle this proble...
Graph neural networks (GNN) has been demonstrated to be effective in classifying graph structures. To further improve the graph representation learning ability, hierarchical GNN has been explored. It leverages the differentiable pooling to cluster nodes into fixed groups, and generates a coarse-grained...
In traditional single-channel-based feature extraction approaches, features are captured solely based on dependency, while semantic similarity and dependency types between words are ignored. Although some success has been achieved through the graph convo
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks(论文解析),程序员大本营,技术文章内容聚合第一站。
Heterogeneous graphical neural network Graph theory is widely used in supply chains to analyze complex networks (Guerrero-Lorente et al., 2020). Suppose a graph network G=(V,E,A), where V is the set of n nodes, E is the set of edges between nodes, and A={zi∣vi∈V} characterizes ...
这篇文章在DiffPool的基础上引入了多通道(Multi-channel)的概念,提出了multi channel graph convolutional networks (MuchGCN),通过inter-channel和intra-channel两种方式聚合通道上的信息。 本文被IJCAI2020接收,在写这篇博客时我看的是2019年12月挂到arXiv上的版本,整体感觉有点粗糙,被会议接... ...
Capsule networkDeep learningElectroencephalogram (EEG)Emotion recognitionIn recent years, deep learning (DL) techniques, and in particular convolutional neural networks (CNNs), have shown great potential in electroencephalograph (EEG)-based emotion recognition. However, existing CNN-based EEG emotion ...