本人精读了事件抽取领域的经典论文《Event Extraction via Dynamic Multi-Pooling Convolutional Neural Network》,并作出我的读书报告。这篇论文由中科院自动化所赵军、刘康等人发表于ACL2015会议,提出了用CNN模型解决事件抽取任务。 在深度学习没有盛行之前,解决事件抽取任务的传统方法,依赖于较为精细的特征设计已经一系列...
Instead of modifying loss function of sparse filtering, we simply introduce two fusion mechanisms into sparse filtering, i.e., multi-scale fusion and multi-pooling fusion. In detail, the former aims to learn different local features from the collected signals under multiple scales. The latter ...
Extracting Biomedical Events with Parallel Multi-Pooling Convolutional Neural Networks Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks. The Meeting of the ... L Li,L Yang,M Qin - 《IEEE/ACM Transactions on ...
Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification 来自 Semantic Scholar 喜欢 0 阅读量: 127 作者:T Yun,G Ding,H Jing,X He,B Zhou 摘要: This paper aims to improve the widely used deep speaker embedding x-vector model. We propose the following ...
MultiRocket Multiple pooling operators and transformations for fast and effective time series classification Preprint: arxiv:2102.00457 We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art performance with a tiny fraction of the time and without the ...
Official implementation of “TENet: Targetness Entanglement Incorporating with Multi-Scale Pooling and Mutually-Guided Fusion for RGB-E Object Tracking” - SSSpc333/TENet
MULTI-CARRIER POWER POOLING 专利名称：MULTI-CARRIER POWER POOLING 发明人：Heinz A. Miranda,Michael H. Baker,James P.Michels,Yong Liu 申请号：US15900119 申请日：20180220 公开号：US20180192378A1 公开日：20180705 专利内容由知识产权出版社提供 专利附图：摘要：Novel techniques for pooling the available ...
Hierarchical Multi-View Graph Pooling with Structure Learning https://github.com/cszhangzhen/MVPoolhttps://cszhangzhen.github.io/ Contributions 提出了一种多视图的图池化操作MVPool,能整合到不同的图神经网络架构中。多视图之间的协作能够产生鲁棒的节点排序,用于池化操作。防止之前方法中单一评价方案造成的bias...
we present a new Web video annotation approach based on multi-instance learning (MIL) with a learnable pooling. By formulating the Web video annotation as a MIL problem, we present a end-to-end deep network framework to solve this problem in which the frame (instance) level an...
bcNN缺乏重要的语义信息。因此提出multi-layer weight-aware bilinear pooling.给每一个卷积层分配一个动态的权重 贡献: effecient interaction of different convlution layer features。分配动态的权重给每一层卷积层。集成多个卷积层来获取更详细的语义信息。