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...
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...
This study introduces a novel temporal convolutional network (TCN) layer and its derived composite model, “improved TCN-BiLSTM-MHA,” which integrates a TCN, bidirectional long short-term memory (BiLSTM) networks, and a multi-head attention mechanism (MHA). The protein sequences are tokenized ...
[AAAI 2023] Self-organization Preserved Graph Structure Learning with Principle of Relevant Information [Paper| Code] [AAAI 2023] USER: Unsupervised Structural Entropy-based Robust Graph Neural Network [Paper | Code] [AAAI 2023] Spatio-Temporal Meta-Graph Learning for Traffic Forecasting [Paper | Co...
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...
Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting 2024, Applied Intelligence Development of human motion prediction strategy using inception residual block 2023, Multimedia Tools and Applications Leak Detection in Water Supply Network Using a Data...
Specifically, we propose a triplet sampling mechanism to encode the local temporal relationship of adjacent frames based on their deep representations. In addition, we incorporate the graph structure of the video, as a priori, to holistically preserve the inher...
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. ...
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, ...