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-22400@www.clsp.jhu.edu DTSTAMP:20240328T212025Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nModern learning architectures for natural language processing have been very suc cessful in incorporating a huge amount of texts into their parameters. How ever\, by and large\, such models store and use knowledge in distributed a nd decentralized ways. This proves unreliable and makes the models ill-sui ted for knowledge-intensive tasks that require reasoning over factual info rmation in linguistic expressions. In this talk\, I will give a few examp les of exploring alternative architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by r epresenting\, storing and accessing knowledge in a dedicated memory compon ent.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Google Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collabo rators in Google Research.
\nBiography
\nFei is a research scientist at Google Research. Before that\, he was a Profess or of Computer Science at University of Southern California. His primary r esearch interests are machine learning and its application to various AI p roblems: speech and language processing\, computer vision\, robotics and r ecently weather forecast and climate modeling. He has a PhD (2007) from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
DTSTART;TZID=America/New_York:20221024T120000 DTEND;TZID=America/New_York:20221024T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fei Sha (University of Southern California) “Extracting Information from Text into Memory for Knowledge-Intensive Tasks” URL:https://www.clsp.jhu.edu/events/fei-sha-university-of-southern-californ ia/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT BEGIN:VEVENT UID:ai1ec-23586@www.clsp.jhu.edu DTSTAMP:20240328T212025Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230410T120000 DTEND;TZID=America/New_York:20230410T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Ruizhe Huang URL:https://www.clsp.jhu.edu/events/student-seminar-ruizhe-huang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Huang END:VEVENT BEGIN:VEVENT UID:ai1ec-23892@www.clsp.jhu.edu DTSTAMP:20240328T212025Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nThe growing power in compu ting and AI promises a near-term future of human-machine teamwork. In this talk\, I will present my research group’s efforts in understanding the co mplex dynamics of human-machine interaction and designing intelligent mach ines aimed to assist and collaborate with people. I will focus on 1) tools for onboarding machine teammates and authoring machine assistance\, 2) me thods for detecting\, and broadly managing\, errors in collaboration\, and 3) building blocks of knowledge needed to enable ad hoc human-machine tea mwork. I will also highlight our recent work on designing assistive\, coll aborative machines to support older adults aging in place.
\nBiography
\nChien-Ming Huang is the John C. Malone Assista nt Professor in the Department of Computer Science at the Johns Hopkins Un iversity. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier venues in HRI\, HCI \, and robotics including Science Robotics\, HRI\, CHI\, and CSCW. His res earch has received media coverage from MIT Technology Review\, Tech Inside r\, and Science Nation. Huang 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/
DTSTART;TZID=America/New_York:20230915T120000 DTEND;TZID=America/New_York:20230915T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Chien-Ming Huang (Johns Hopkins University) “Becoming Teammates: De signing Assistive\, Collaborative Machines” URL:https://www.clsp.jhu.edu/events/chien-ming-huang-johns-hopkins-universi ty/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24479@www.clsp.jhu.edu DTSTAMP:20240328T212025Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nT he speech field is evolving to solve more challenging scenarios\, such as multi-channel recordings with multiple simultaneous talkers. Given the man y types of microphone setups out there\, we present the UniX-Encoder. It’s a universal encoder designed for multiple tasks\, and worked with any mic rophone array\, in both solo and multi-talker environments. Our research e nhances previous multichannel speech processing efforts in four key areas: 1) Adaptability: Contrasting traditional models constrained to certain mi crophone array configurations\, our encoder is universally compatible. 2) MultiTask Capability: Beyond the single-task focus of previous systems\, U niX-Encoder acts as a robust upstream model\, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervise d Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to models that first beamfor m then process single-channels\, our encoder offers an end-to-end solution \, bypassing explicit beamforming or separation. To validate its effective ness\, we tested the UniXEncoder on a synthetic multi-channel dataset from the LibriSpeech corpus. Across tasks like speech recognition 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-TAGS;LANGUAGE=en-US:2024\,Huang\,March END:VEVENT END:VCALENDAR