利用小波变换、注意力机制和LSTM对BGP数据进行异常检测(有监督)。 2.2 写作动机 之前人们使用LSTM做时间序列异常检测多是用stack-LSTM,这样带来的问题是参数量较大,并且模型过于复杂。本文作者首先使用小波变换提取出原始序列的多尺度信息,然后用LSTM和注意力机制提取各个尺度的信息,最后用单层LSTM做分类。
A framework called Multi-scale RNNs specifically addresses the issue of learning long term dependencies in RNNs. Following that approach, we devised a LSTM-based Multi-scale model that learns to build different temporal scales from the original wind speed series that are then used as input for ...
受音乐多分辨率特性的启发,我们还在 AE 编码器中引入了一种新颖的多分辨率 LSTM(MRLSTM)组件。它旨在以多种时间分辨率分析钢琴卷轴。在我们的实现中,MRLSTM 在 1:1、1:2 和 1:4 的分辨率下工作。最低分辨率(1:4)专注于每第四个时间片,在典型的简单时间节奏中,它将具有较高的度量权重。 4.3 潜在空间中的...
某些特征在A任务上难以学习,可能A对标的loss无法有效地利用这种特征,但在另一个任务B上就很好学习,任务B可以更好地把这些特征represent出来,比如说我们用lstm做time series的feature extraction,把结果给下游的lightgbm来用。到mtl里,model用task来代替,并且这个过程是端到端的。 (4)更紧凑的假设空间 比如说对任务A...
Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models. 文本分类对主题进行分层分类是一个常见而实用的问题。传统的方法只是简单地使用词汇袋,取得了良好的效果...
the LSTM was used to extract association based on the output of CNN. This means it is possible to loss important spatio-temporal features. This is unbearable on the limited poor dataset, specially. Thus, in this paper, we propose a novel multi-scale CNN and bi-LSTM arbitration dense network...
MSCL-Attention: A Multi-Scale Convolutional Long Short-Term Memory (LSTM) Attention Network for Predicting CO2 Emissions from Vehicles MSCL-Attention: A Multi-Scale Convolutional Long Short-Term Memory (LSTM) Attention Network for Predicting CO2 Emissions from VehiclesThe transportation industry is one ...
In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-
In this study, an intelligent deep learning method, systematically blending the dispersion entropy-based multi-scale series aggregation scheme and long short term memory (LSTM) neural network, is proposed for forecasting the health evolution trend of aero-engine. Firstly, a comprehensive measurement of...
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognition 不对劲大家 2 人赞同了该文章 研究目标: 近年来,由于从骨骼数据中提取时空特征的能力有限,基于卷积神经网络或循环神经网络的方法识别精度较差。一系列基于图卷积网络(GCN)的方法取得了显着的性能并逐渐占据主导地位。然而...