To tackle these challenges, this paper introduces the Temporal Convolutional Attention Network (TCAN) framework. This framework harmoniously combines a temporal convolutional network (TCN) with an ...

To solve abovementioned problems, a novel deep learning based spatio-temporal graph convolutional neural network (STGCN) is developed for intelligent fault diagnosis of wind turbines in this paper.

We propose a novel unified spatial–temporal regression framework named Generalized Spatial–Temporal Regression Graph Convolutional Transformer (GSTRGCT) that extends panel model in spatial ...

Temporal Technologies Inc ... by a second application on which the workload relies to run. Errors in the network that connects the workload to the second application can likewise lead to an ...

2025 年3月，狄耐克脑电波交互事业部一行前往厦门大学“脑认知与智能计算实验室”开展参观交流活动。此次受邀参观，是狄耐克在推动脑电波交互技术实际应用进程中的重要探索，加速相关技术在睡眠健康领域的创新与发展。

Seattle-based Temporal has made its name over the last several years in the world of microservices — specifically providing a platform to orchestrate the messy business of building and operating ...

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.) ...

A Convolutional Neural Network (CNN) is a form of artificial intelligence that plays a key role in the AI ecosytem due to its ability to analyze and understand visual data. The need to decipher ...

The key to addressing these challenges lies in separating the encoder and decoder components of multimodal machine learning models.