The approach also applies a semi-supervised learning process to re-train knowledge-to-visual model.
The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.
We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.
Federated Learning (FL), arising as a novel secure learning paradigm, has received notable attention from the public.
The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.
The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.
We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing.
Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models.
Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR).
In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments.
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages.
In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment.
Different from prior methods, we calculate the saliency value of each node based on the relationship between the corresponding node and the virtual node.