5 papers
Dual-Domain Representation Alignment: Bridging 2D and 3D Vision via Geometry-Aware Architecture Search
Haoyu Zhang, Zhihao Yu, Rui Wang +3
Modern computer vision requires balancing predictive accuracy with real-time efficiency, yet the high inference cost of large vision models (LVMs) limits deployment on resource-con…
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Mengmeng Chen, Xiaohu Wu, Qiqi Liu +5
Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e.g., it is fundamental for…
Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning
Mengmeng Chen, Xiaohu Wu, Xiaoli Tang +5
Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data het…
Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal Systems
Guodong Chen, Jiu Jimmy Jiao, Qiqi Liu +2
Multi-objective optimization has burgeoned as a potent methodology for informed decision-making in enhanced geothermal systems, aiming to concurrently maximize economic yield, ensu…
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
Zhilong Li, Xiaohu Wu, Xiaoli Tang +6
There is growing research interest in measuring the statistical heterogeneity of clients' local datasets. Such measurements are used to estimate the suitability for collaborative t…