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-21259@www.clsp.jhu.edu DTSTAMP:20240328T125245Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNatural language processing has been revolutionized b y neural networks\, which perform impressively well in applications such a s machine translation and question answering. Despite their success\, neur al networks still have some substantial shortcomings: Their internal worki ngs are poorly understood\, and they are notoriously brittle\, failing on example types that are rare in their training data. In this talk\, I will use the unifying thread of hierarchical syntactic structure to discuss app roaches for addressing these shortcomings. First\, I will argue for a new evaluation paradigm based on targeted\, hypothesis-driven tests that bette r illuminate what models have learned\; using this paradigm\, I will show that even state-of-the-art models sometimes fail to recognize the hierarch ical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\, I will show how these beh avioral failings can be explained through analysis of models’ inductive bi ases and internal representations\, focusing on the puzzle of how neural n etworks represent discrete symbolic structure in continuous vector space. I will close by showing how insights from these analyses can be used to ma ke models more robust through approaches based on meta-learning\, structur ed architectures\, and data augmentation.\nBiography\nTom McCoy is a PhD c andidate in the Department of Cognitive Science at Johns Hopkins Universit y. As an undergraduate\, he studied computational linguistics at Yale. His research combines natural language processing\, cognitive science\, and m achine learning to study how we can achieve robust generalization in model s of language\, as this remains one of the main areas where current AI sys tems fall short. In particular\, he focuses on inductive biases and repres entations of linguistic structure\, since these are two of the major compo nents that determine how learners generalize to novel types of input. DTSTART;TZID=America/New_York:20220131T120000 DTEND;TZID=America/New_York:20220131T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Tom McCoy (Johns Hopkins University) “Opening the Black Box of Deep Learning: Representations\, Inductive Biases\, and Robustness” URL:https://www.clsp.jhu.edu/events/tom-mccoy-johns-hopkins-university-open ing-the-black-box-of-deep-learning-representations-inductive-biases-and-ro bustness/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nNatural language processing has been revolutionized b y neural networks\, which perform impressively well in applications such a s machine translation and question answering. Despite their success\, neur al networks still have some substantial shortcomings: Their internal worki ngs are poorly understood\, and they are notoriously brittle\, failing on example types that are rare in their training data. In this talk\, I will use the unifying thread of hierarchical syntactic structure to discuss app roaches for addressing these shortcomings. First\, I will argue for a new evaluation paradigm based on targeted\, hypothesis-driven tests that bette r illuminate what models have learned\; using this paradigm\, I will show that even state-of-the-art models sometimes fail to recognize the hierarch ical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\, I will show how these beh avioral failings can be explained through analysis of models’ inductive bi ases and internal representations\, focusing on the puzzle of how neural n etworks represent discrete symbolic structure in continuous vector space. I will close by showing how insights from these analyses can be used to ma ke models more robust through approaches based on meta-learning\, structur ed architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Sci ence at Johns Hopkins University. As an undergraduate\, he studied computa tional linguistics at Yale. His research combines natural language process ing\, cognitive science\, and machine learning to study how we can achieve robust generalization in models of language\, as this remains one of the main areas where current AI systems fall short. In particular\, he focuses on inductive biases and representations of linguistic structure\, since t hese are two of the major components that determine how learners generaliz e to novel types of input.
\n X-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-23320@www.clsp.jhu.edu DTSTAMP:20240328T125245Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSpeech communications represents a core domain for ed ucation\, team problem solving\, social engagement\, and business interact ions. The ability for Speech Technology to extract layers of knowledge and assess engagement content represents the next generation of advanced spee ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we ll as voice enabled speech systems have required the need for effective vo ice capture and automatic speech/speaker recognition. The ability to emplo y speech and language technology to assess human-to-human interactions off ers new research paradigms having profound impact on assessing human inter action. In this talk\, we will focus on big data naturalistic audio proces sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna r missions. ML based technology advancements include automatic audio diari zation\, speech recognition\, and speaker recognition. Child-Teacher based assessment of conversational interactions are explored\, including keywor d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio ns are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor pus\, resulting in a massive multi-track audio processing challenge to mak e available 150\,000hrs of Apollo mission data to be shared with science c ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba sed researchers\, and (iii) education/historical/archiving specialists. Ou r goals here are to provide resources which allow to better understand how people work/learn collaboratively together. For Apollo\, to accomplish on e of mankind’s greatest scientific/technological challenges in the last ce ntury.\nBiography\nJohn H.L. Hansen\, received Ph.D. & M.S. degrees from G eorgia Institute of Technology\, and B.S.E.E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 2005\, where he currently serves a s Associate Dean for Research\, Prof. of ECE\, Distinguished Univ. Chair i n Telecom. Engineering\, and directs Center for Robust Speech Systems (CRS S). He is an ISCA Fellow\, IEEE Fellow\, and has served as Member and TC-C hair of IEEE Signal Proc. Society\, Speech & Language Proc. Tech. Comm.(SL TC)\, and Technical Advisor to U.S. Delegate for NATO (IST/TG-01). He serv ed as ISCA President (2017-21)\, continues to serve on ISCA Board (2015-23 ) as Treasurer\, has supervised 99 PhD/MS thesis candidates (EE\,CE\,BME\, TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sci)\, was recipient of 2020 UT-D allas Provost’s Award for Grad. PhD Research Mentoring\; author/co-author of 865 journal/conference papers including 14 textbooks in the field of sp eech/language/hearing processing & technology including coauthor of textbo ok Discrete-Time Processing of Speech Signals\, (IEEE Press\, 2000)\, and lead author of the report “The Impact of Speech Under ‘Stress’ on Military Speech Technology\,” (NATO RTO-TR-10\, 2000). He served as Organizer\, Ch air/Co-Chair/Tech.Chair for ISCA INTERSPEECH-2022\, IEEE ICASSP-2010\, IEE E SLT-2014\, ISCA INTERSPEECH-2002\, and Tech. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processing Society Leo Beranek MERITORIO US SERVICE Award.\n DTSTART;TZID=America/New_York:20230303T120000 DTEND;TZID=America/New_York:20230303T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:John Hansen (University of Texas at Dallas) “Challenges and Advance ments in Speaker Diarization & Recognition for Naturalistic Data Streams” URL:https://www.clsp.jhu.edu/events/john-hansen-university-of-texas-at-dall as/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nSpeech communications represents a core domain for ed ucation\, team problem solving\, social engagement\, and business interact ions. The ability for Speech Technology to extract layers of knowledge and assess engagement content represents the next generation of advanced spee ch solutions. Today\, the emergence of BIG DATA\, Machine Learning\, as we ll as voice enabled speech systems have required the need for effective vo ice capture and automatic speech/speaker recognition. The ability to emplo y speech and language technology to assess human-to-human interactions off ers new research paradigms having profound impact on assessing human inter action. In this talk\, we will focus on big data naturalistic audio proces sing relating to (i) child learning spaces\, and (ii) the NASA APOLLO luna r missions. ML based technology advancements include automatic audio diari zation\, speech recognition\, and speaker recognition. Child-Teacher based assessment of conversational interactions are explored\, including keywor d and “WH-word” (e.g.\, who\, what\, etc.). Diarization processing solutio ns are applied to both classroom/learning space child speech\, as well as massive APOLLO data. CRSS-UTDallas is expanding our original Apollo-11 cor pus\, resulting in a massive multi-track audio processing challenge to mak e available 150\,000hrs of Apollo mission data to be shared with science c ommunities: (i) speech/language technology\, (ii) STEM/science and team-ba sed researchers\, and (iii) education/historical/archiving specialists. Ou r goals here are to provide resources which allow to better understand how people work/learn collaboratively together. For Apollo\, to accomplish on e of mankind’s greatest scientific/technological challenges in the last ce ntury.
\nBiography
\nJohn H.L. Hansen\, recei ved Ph.D. & M.S. degrees from Georgia Institute of Technology\, and B.S.E. E. from Rutgers Univ. He joined Univ. of Texas at Dallas (UTDallas) in 200 5\, where he currently serves as Associate Dean for Research\, Prof. of EC E\, Distinguished Univ. Chair in Telecom. Engineering\, and directs Center for Robust Speech Systems (CRSS). He is an ISCA Fellow\, IEEE Fellow\, an d has served as Member and TC-Chair of IEEE Signal Proc. Society\, Speech & Language Proc. Tech. Comm.(SLTC)\, and Technical Advisor to U.S. Delegat e for NATO (IST/TG-01). He served as ISCA President (2017-21)\, continues to serve on ISCA Board (2015-23) as Treasurer\, has supervised 99 PhD/MS t hesis candidates (EE\,CE\,BME\,TE\,CS\,Ling.\,Cog.Sci.\,Spch.Sci.\,Hear.Sc i)\, was recipient of 2020 UT-Dallas Provost’s Award for Grad. PhD Researc h Mentoring\; author/co-author of 865 journal/conference papers including 14 textbooks in the field of speech/language/hearing processing & technolo gy including coauthor of textbook Discrete-Time Processing of Speech Signa ls\, (IEEE Press\, 2000)\, and lead author of the report “The Impact of Sp eech Under ‘Stress’ on Military Speech Technology\,” (NATO RTO-TR-10\, 200 0). He served as Organizer\, Chair/Co-Chair/Tech.Chair for ISCA INTERSPEEC H-2022\, IEEE ICASSP-2010\, IEEE SLT-2014\, ISCA INTERSPEECH-2002\, and Te ch. Chair for IEEE ICASSP-2024. He received the 2022 IEEE Signal Processin g Society Leo Beranek MERITORIOUS SERVICE Award.
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