The systems and devices further including a processor to generate driving series based on observations from the video sequences, and generate predictions of future events based on the observations using a dual-stage attention-based recurrent neural network (DA-RNN). The DA-RNN includes an input ...
2. select the relevant driving series to make a prediction We propose a dual-stage attention-based RNN to address these 2 issues. 1. first stage: input attention mechanism to extract relevant driving series. 2. second stage: temporal attention mechanism. attention-based encoder-decoder networks f...
dual_stage_attention_rnn In an attempt to learn Tensorflow, I have implemented the model inA Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictionusing Tensorflow 1.13. Nasdaq data is used for testing, which is from repoda-rnn. ...
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction - sunfanyunn/DARNN
To improve prediction accuracy, computational efficiency, and interpretability, and to address feature redundancy and high nonlinearity from multiple data sources, we propose DAMixer—a dual-stage attention-based mixer model for multivariate time series forecasting. This model leverages feature attention to...
Dual-stage Attention-based Recurrent Neural Network 金融时间序列可以从市场情绪(market sentiment)、时间相关性(temporal dependencies)、流动性(temporal dependencies)等多个方面进行解释。因此,技术指标,如移动平均线(moving average)、随机震荡指标(stochastic oscillators),经常被用来从原始市场数据中提取特征。我们首先...
Specifically, we have formulated this problem as a divide-conquer search for face parts guided by a learned FLM model using CNNs in a hierarchy, whereas a multi-stage cascaded regression based on CNN was used to predict the landmark location within each individual part. The whole framework ...
Dual-stage two-phase attention-based recurrent neural network (DSTP) [19] has made improvements to this problem of DARNN and optimized the prediction effect of long sequences. However, DSTP still does not make effective use of long sequences. When the time window size is small, the series ...
The subsequent stage utilizes ViT as a deep feature extractor, adept at modeling the global aspects of EEG signals and employing attention mechanisms for precise classification. We also present an innovative algorithm for data mapping in transfer learning, ensuring consistent feature representation across...
The role of robots in society keeps expanding, bringing with it the necessity of interacting and communicating with humans. In order to keep such interacti