GANs has received wide attention due to their outstanding ability to produce realistic-looking images and can implicitly learn rich distributions over Spatio-Temporal (ST) correlations in Multivariate Time Series (MTS). Forecasting using MTS remains a challenge that needs to be solved to make ...
To overcome these limitations and therefore enhance the spatial and temporal features extraction for action recognition, we propose a novel Spatial Attention-Enhanced Multi-Timescale Graph Convolutional Network (SA-MTGCN) for skeleton-based action recognition. Specifically, as the relation of non-adjacent...
The attention-based encoder–decoder was proposed by Bahdanau et al. [25], which allowed hidden states from the encoder can be selected by their importance to the decoder. Over the past few years, attention mechanisms have shown their superiority in many tasks in real-world scenarios. For exam...
and associate them in crowds simultaneously. A coarse-to-fine process is designed to gradually apply the attention module on the aggregated multi-scale feature maps to enforce the network to exploit the discriminative space-time features for better performance. The whole network is trained in an en...
Moreover, the efficient interconnection via metropolitan area networks to the Internet at large has received increasing attention [32,33]. This case study considers the LayBack architecture [12] which can encompass the layering structures of a wide range of other proposed architectures and wireless ...
After being processed by the dynamic graph attention network, the data area passed through a time–frequency graph pooling layer to enhance feature learning. Finally, a fully connected layer recognizes the fault states. The main contributions of this study are summarized as follows: (1) A new ...
Linear attentionAdaptive fusionTransformer forecasterMultivariate time seriesRecently, the prosperity of linear models has raised questions about capturing the sequential capabilities of Transformer forecasters. Although the latest Transformer-based studies have alleviated some of these concerns, the limited ...
Time series forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for...doi:10.1007/978-981-10-2777-2_5Waddah Waheeb...
摘要: In this paper, we propose a novel approach for financial time series forecasting using feature selection, image encoding, and a self-attention-based CNN transformer. We use Markov transition field ...关键词: candlestick image encoding computer vision convolutional neural network transformer ...
single CPU.Index Terms: Speech synthesis, sequence-to-sequence model,attention, transfer learning, vocoder, LPCNet1. IntroductionIn the past few years, speech synthesis has attracted a lot of at-tention due to advances in deep learning. Sequence-to-sequenceneural network [1] with attention ...