temporal convolutional neural network structuretemporal convolutional neural network structure TemporalConvolutionalNeuralNetwork(TCN)结构是一种新型的神经网络结构,能够有效地处理时间序列数据。该结构在许多领域应用广泛,如语音识别、自然语言处理、动作识别等。 TCN
Thus, the purpose of providing additional information for decision making and labor cost saving is achieved. This study proposed a more accurate traffic velocity prediction model, named Spatial-Temporal Tree-structure Dual-channel Convolution Network. The model designs a spatial tree convolution module ...
In this context, we propose an approach that success- fully takes into account both the local and global temporal structure of videos to produce descriptions. First, our ap- proach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short tem- poral...
In this section we present the architecture of the neural network model we use to generate shared feature-structure node embeddings.Footnote1We take a featured network as input, with structure represented as an adjacency matrix and node features represented as vectors (see below for a formal defini...
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein
In Holme and Saramäki (2012), the authors present the emergent field of temporal networks and discuss methods for analyzing the topological and the temporal structure and models for illustrating their link with the dynamic behavior of social networks. A dynamic network is designed as a dynamic ...
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein
Generating coherent patterns of activity from chaotic neural networks. Neuron 63, 544–557 (2009). Article Google Scholar Tyukin, I. Y., Prokhorov, D. & Van Leeuwen, C. Adaptive classification of temporal signals in fixed-weight recurrent neural networks: an existence proof. Neural Comput. ...
Generally, there are two typical ways of dynamic structure learning, i.e., using recurrent architectures [9, 10] and capturing temporal patterns [11, 12]. However, 6.4 Incremental Learning on Growing Data 115 the efficient learning of temporally growing structure has not been explored yet, ...
To make use of the SSI, a fully convolutional network (FCN) is designed to hierarchically learn the SSI. Simulation results show that the better detection performance can be obtained with the proposed SSI-based target detection method comparing to the TTD method. The justifications of using the ...