587 citations · 2k across the 58 of their papers we have counts for
17 papers · 1 filter
Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions
Felix Lanfermann, Qiqi Liu, Yaochu Jin +1
Implementing resource efficient energy management systems in facilities and buildings becomes increasingly important in the transformation to a sustainable society. However, select…
A Graph Neural Network with Negative Message Passing for Graph Coloring
Xiangyu Wang, Xueming Yan, Yaochu Jin
Graph neural networks have received increased attention over the past years due to their promising ability to handle graph-structured data, which can be found in many real-world pr…
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location
Shiqing Liu, Xueming Yan, Yaochu Jin
The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical…
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures
Jia Liu, Ran Cheng, Yaochu Jin
Deep neural networks have been found vulnerable to adversarial attacks, thus raising potentially concerns in security-sensitive contexts. To address this problem, recent research h…
Recent Advances in Bayesian Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the dev…
A Survey on Computationally Efficient Neural Architecture Search
Shiqing Liu, Haoyu Zhang, Yaochu Jin
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users wit…
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features
Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas
Self-supervised learning (SSL) has become a popular method for generating invariant representations without the need for human annotations. Nonetheless, the desired invariant repre…
Federated Learning on Non-IID Data: A Survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu +1
Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than…
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search
Guoyang Xie, Jinbao Wang, Guo Yu +2
Due to limited computational cost and energy consumption, most neural network models deployed in mobile devices are tiny. However, tiny neural networks are commonly very vulnerable…
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks
Jia Liu, Yaochu Jin
Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans. To address this issue, various methods have been proposed to design netw…
Generation of Consistent Sets of Multi-Label Classification Rules with a Multi-Objective Evolutionary Algorithm
Thiago Zafalon Miranda, Diorge Brognara Sardinha, Márcio Porto Basgalupp +2
Multi-label classification consists in classifying an instance into two or more classes simultaneously. It is a very challenging task present in many real-world applications, such…
Real-time Federated Evolutionary Neural Architecture Search
Hangyu Zhu, Yaochu Jin
Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is t…
Ternary Compression for Communication-Efficient Federated Learning
Jinjin Xu, Wenli Du, Ran Cheng +2
Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of…
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…
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…
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…
Multi-objective Evolutionary Federated Learning
Hangyu Zhu, Yaochu Jin
Federated learning is an emerging technique used to prevent the leakage of private information. Unlike centralized learning that needs to collect data from users and store them col…