翻译在:2018-GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs 有一个mxnet的实现:https://github.com/jennyzhang0215/GaAN 文章贡献: 我们提出了一种新的网络结构,门注意网络(GaAN),用于图的学习。GaAN不同于传统的多头注意机制(它均衡的消耗所有的注意头),它使用一个卷积子网络...
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 ...
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...
近年来时序数据处理的一些发展 许多最近的机器学习模型,包括Temporal convolutional networks[19])(rTemporal convolutional networksfor action segmentation and detection,)、核学习[20](i, Large scale online multiple kernel regression withapplication to time-series prediction, )和transformer[21]( Attend and diagn...
Recent architectural developments have enabled recurrent neural networks (RNNs) to reach and even surpass the performance of Transformers on certain sequence modeling tasks. These modern RNNs feature a prominent design pattern: linear recurrent layers interconnected by feedforward paths with multiplicative ...
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...
Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial Training 使用门控多级注意力和时间对抗训练增强transformer进行视频理解 CVPR2021 Gated Adversarial Transformer (GAT)来提高att... 查看原文 对抗生成网络大集合!论文、视频、代码、研讨会一网打尽! Understanding ...
To do this, we use an attention-based recurrent neural network to capture the temporal features of hand motion to unveil the underlying pattern between the gesture and these sequences. While the attention layers capture patterns from the weights of the short term, the gated recurrent unit (GRU...
In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 201...
The EEG-GCN approaches time series data as a one-dimensional temporal signal, applying a dual-layered signal decomposition using both Ensemble Empirical Mode Decomposition (EEMD) and GRU. This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into...