Therefore, we present a spatial-temporal gated graph attention network (ST-GGANet) to learn the spatial-temporal patterns of skeleton sequences. The proposed approach uses a lightweight self-attention-based gate layer to pay attention to the important body parts or joints of human skeleton ...
We propose a new channel-fused gated temporal convolutional network. First, a channel fusion and gating mechanism is designed to improve temporal convolutional networks, allowing the model to obtain higher-level features. Second, we improve the channel fusion module by the short-term average energy ...
Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a da...
In this paper, a Bidirectional Gated Temporal Convolution Attention model is proposed for text classification, and the main works of this model are feature extraction and feature aggregation. The text features are extracted using the bidirectional temporal convolution to solve the problem that the exist...
ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting Accurate traffic state prediction plays an important role in traffic guidance, travel planning, etc. Due to the existence of complex spatio-temporal relati... J Yang,J Li,MY Shen - 《Journal of ...
(2019). Self-attention networks for connectionist temporal classification in speech recognition. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 7115–7119): IEEE. Sharma, N., & Yalla, P. (2018). Developing research questions in natural...
Then, we propose an integrated model, JGC_MMN (Joint Gated Co-attention Based Multi-modal Network), to learn all-level features and capture spatiotemporal correlations in all-time stages with a modified densely connected convolutional network as well as current ingredients and future expectations. ...
Temporal relationships GRU is a type of recurrent neural network that is particularly good at capturing temporal dependencies, even over long sequences, which is vital for time-series prediction. Modeling dynamics It allows the model to include the dynamics of the system being studied, learning when...
4.5 Self-Attention (SA) evoked bi-gated recurrent (BiGRU) networks: Bi networks are built as a BiGRU network, which consists of forward and backward GRU, to extract the temporal information from the raw sine waves. Equation (4) provides information about the mathematical properties of the BiGRU...
Zhang Q, Nicolson A, Wang M, et al (2019) Monaural speech enhancement using a multi-branch temporal convolutional network.arXiv:1912.12023 Zhang Y, Yang Q (2018) An overview of multi-task learning. National Sci Rev 5(1):30–43