587 citations · 2k across the 58 of their papers we have counts for
16 papers · 1 filter
Intelligent Computing: The Latest Advances, Challenges and Future
Shiqiang Zhu, Ting Yu, Tao Xu +18
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm…
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…
A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm
Qiqi Liu, Yuping Yan, Peter Ligeti +1
Data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving ma…
Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning
Yitian Hong, Yaochu Jin, Yang Tang
In cooperative multi-agent reinforcement learning, centralized training and decentralized execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) decompositio…
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimizat…
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Nan Li, Lianbo Ma, Guo Yu +3
Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often n…
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
Zhichao Lu, Ran Cheng, Yaochu Jin +2
The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the develo…
Towards Fairness-Aware Multi-Objective Optimization
Guo Yu, Lianbo Ma, Wei Du +2
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. Howev…
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 Survey of Visual Sensory Anomaly Detection
Xi Jiang, Guoyang Xie, Jinbao Wang +4
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semanti…
Cross-Modality Neuroimage Synthesis: A Survey
Guoyang Xie, Yawen Huang, Jinbao Wang +4
Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modali…
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…
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
Jinbao Wang, Guoyang Xie, Yawen Huang +3
The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases. However, collecting the full set of we…
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis
Jinbao Wang, Guoyang Xie, Yawen Huang +4
Utilizing multi-modal neuroimaging data has been proved to be effective to investigate human cognitive activities and certain pathologies. However, it is not practical to obtain th…