March 25, 2024, 4:41 a.m. | Minghui Qiu (DSA, Hong Kong University of Science,Technology,Guangzhou), Yandao Huang (CSE, Hong Kong University of Science,Technology), Lin Chen (DSA

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.14922v1 Announce Type: new
Abstract: In recent years, emerging research on mobile sensing has led to novel scenarios that enhance daily life for humans, but dynamic usage conditions often result in performance degradation when systems are deployed in real-world settings. Existing solutions typically employ one-off adaptation schemes based on neural networks, which struggle to ensure robustness against uncertain drifting conditions in human-centric sensing scenarios. In this paper, we propose CODA, a COst-efficient Domain Adaptation mechanism for mobile sensing that addresses …

abstract arxiv cost cs.lg cs.ni daily domain domain adaptation dynamic humans life mobile networks neural networks novel performance research sensing solutions systems test type usage world

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineering Manager, Generative AI - Characters

@ Meta | Bellevue, WA | Menlo Park, CA | Seattle, WA | New York City | San Francisco, CA

Senior Operations Research Analyst / Predictive Modeler

@ LinQuest | Colorado Springs, Colorado, United States