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:20240328T095158Z 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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\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.
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\nChien-Ming Huang is the John C. Malone Assistant Professor in the Departm ent of Computer Science at the Johns Hopkins University. His research focu ses on designing interactive AI aimed to assist and collaborate with peopl e. He publishes in top-tier venues in HRI\, HCI\, and robotics including S cience Robotics\, HRI\, CHI\, and CSCW. His research has received media co verage from MIT Technology Review\, Tech Insider\, and Science Nation. Hua ng completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www .cs.jhu.edu/~cmhuang/
\n X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24479@www.clsp.jhu.edu DTSTAMP:20240328T095158Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nThe speech field is evolving to solve more challengin g scenarios\, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out there\, we present the UniX-Encoder. It’s a universal encoder designed for multiple tasks\, a nd worked with any microphone array\, in both solo and multi-talker enviro nments. Our research enhances previous multichannel speech processing effo rts in four key areas: 1) Adaptability: Contrasting traditional models con strained to certain microphone array configurations\, our encoder is unive rsally compatible. 2) MultiTask Capability: Beyond the single-task focus o f previous systems\, UniX-Encoder acts as a robust upstream model\, adeptl y extracting features for diverse tasks including ASR and speaker recognit ion. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to mod els that first beamform then process single-channels\, our encoder offers an end-to-end solution\, bypassing explicit beamforming or separation. To validate its effectiveness\, we tested the UniXEncoder on a synthetic mult i-channel dataset from the LibriSpeech corpus. Across tasks like speech re cognition and speaker diarization\, our encoder consistently outperformed combinations like the WavLM model with the BeamformIt frontend. DTSTART;TZID=America/New_York:20240311T200500 DTEND;TZID=America/New_York:20240311T210500 SEQUENCE:0 SUMMARY:Zili Huang (JHU) “Unix-Encoder: A Universal X-Channel Speech Encode r for Ad-Hoc Microphone Array Speech Processing” URL:https://www.clsp.jhu.edu/events/zili-huang-jhu-unix-encoder-a-universal -x-channel-speech-encoder-for-ad-hoc-microphone-array-speech-processing/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe speech field is evolving to solve more challenging scenarios\, such as multi-channel recordings wi th multiple simultaneous talkers. Given the many types of microphone setup s out there\, we present the UniX-Encoder. It’s a universal encoder design ed for multiple tasks\, and worked with any microphone array\, in both sol o and multi-talker environments. Our research enhances previous multichann el speech processing efforts in four key areas: 1) Adaptability: Contrasti ng traditional models constrained to certain microphone array configuratio ns\, our encoder is universally compatible. 2) MultiTask Capability: Beyon d the single-task focus of previous systems\, UniX-Encoder acts as a robus t upstream model\, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integr ation: In contrast to models that first beamform then process single-chann els\, our encoder offers an end-to-end solution\, bypassing explicit beamf orming or separation. To validate its effectiveness\, we tested the UniXEn coder on a synthetic multi-channel dataset from the LibriSpeech corpus. Ac ross tasks like speech recognition and speaker diarization\, our encoder c onsistently outperformed combinations like the WavLM model with the Beamfo rmIt frontend.
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