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结构采用了卷积神经网络(CNN)的思想,通过一系列卷积层来...
Then, in order to automatically generate the temporal features, a tree-structure network is designed to derive the temporal dependence of nearby readings. The extracted features are fed into the fully connected layer, which can jointly learn the residents labels and the activity labels simultaneously...
In autonomous driving scenarios,pedestrian trajectory prediction is an important research direction.Based on the spatio-temporal graph convolutional neural network,we propose a new pedestrian trajectory prediction algorithm.The new algor... Dong,Y Cao,Fu - 《Journal of Physics Conference》 被引量: 0发...
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 functions by leveraging sequence features extracted fr...
Filtjens B, Vanrumste B, Slaets P (2022) Skeleton-based action segmentation with multi-stage spatial-temporal graph convolutional neural networks. IEEE Transactions on Emerging Topics in Computing, pp 1–11 (2022) https://doi.org/10.1109/TETC.2022.3230912 Tan R, Gao L, Khan N, Guan L (20...
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. ...
Multi-step prediction of chlorophyll concentration based on adaptive graph-temporal convolutional network with series decomposition This paper proposes a time-series decomposition adaptive graph-time convolutional network prediction model. Firstly, the original sequence is decomposed into ... Y Chen,H Zhang...
The temporal structure inference network is built upon a 3D fully convolutional architecture: it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution. The low resolution result provides temporal guidance to the spatial detail recovering network, ...
In the research related to global climate / environmental change, land-cover products in a wide range of temporal and spatial scales are still very important (Huang et al 2021b). Time series MODIS images are often used in land cover dynamic monitoring, vegetation dynamics and so on. For ...
Compared to traditional convolutional neural networks (CNNs) and recurrent neural networks (RNNs), Transformer exhibits unique design characteristics that enable better modeling of long-range feature dependencies and simultaneous capture of spatiotemporal correlations. In recent years, Transformer has achieve...