587 citations · 2k across the 58 of their papers we have counts for
7 papers · 1 filter
A Survey of Deep Learning Applications to Autonomous Vehicle Control
Sampo Kuutti, Richard Bowden, Yaochu Jin +2
Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability…
CMSN: Continuous Multi-stage Network and Variable Margin Cosine Loss for Temporal Action Proposal Generation
Yushuai Hu, Yaochu Jin, Runhua Li +1
Accurately locating the start and end time of an action in untrimmed videos is a challenging task. One of the important reasons is the boundary of action is not highly distinguisha…
Identification of Interaction Clusters Using a Semi-supervised Hierarchical Clustering Method
Yu Chen, Yuanyuan Yang, Yaochu Jin +1
Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to…
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Cheng He, Shihua Huang, Ran Cheng +2
Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is hig…
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
Cheng He, Shihua Huang, Ran Cheng +2
Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.Usually, the performance of such model based evolutionary algorithms is h…
A Fast Differential Grouping Algorithm for Large Scale Black-Box Optimization
Zhigang Ren, An Chen, Yaochu Jin +3
Decomposition plays a significant role in cooperative co-evolution which shows great potential in large scale black-box optimization. However, current popular decomposition algorit…
Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
Yang Chen, Xiaoyan Sun, Yaochu Jin
Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their…