BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-22408@www.clsp.jhu.edu DTSTAMP:20240329T075112Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAI-powered applications increasingly adopt Deep Neura l Networks (DNNs) for solving many prediction tasks\, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased co mputation\, energy\, and storage costs. An effective approach to address t he problem is multi-task learning (MTL) where a set of tasks are learned j ointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly redu ced inference costs and improved generalization performance in many machin e learning applications. In this talk\, we will introduce our recent effor ts on leveraging MTL to improve accuracy and efficiency for edge computing . The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low infer ence costs and high task accuracy.\n\nBiography\n\n\nHui Guan is an Assist ant Professor in the College of Information and Computer Sciences (CICS) a t the University of Massachusetts Amherst\, the flagship campus of the UMa ss system. She received her Ph.D. in Electrical Engineering from North Car olina State University in 2020. Her research lies in the intersection betw een machine learning and systems\, with an emphasis on improving the speed \, scalability\, and reliability of machine learning through innovations i n algorithms and programming systems. Her current research focuses on both algorithm and system optimizations of deep multi-task learning and graph machine learning. DTSTART;TZID=America/New_York:20221111T120000 DTEND;TZID=America/New_York:20221111T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hui Guan (University of Massachusetts Amherst) “Towards Accurate an d Efficient Edge Computing Via Multi-Task Learning” URL:https://www.clsp.jhu.edu/events/hui-guan-university-of-massachusetts-am herst/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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