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-20730@www.clsp.jhu.edu DTSTAMP:20240328T090955Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nRaytheon BBN participated in the IARPA MATERIAL program\, whose objective is to enable rapid develop ment of language-independent methods for cross-lingual information retriev al (CLIR). The challenging CLIR task of retrieving documents written (or s poken) in one language so that they satisfy an information need expressed in a different language is exacerbated by unique challenges posed by the M ATERIAL program: limited training data for automatic speech recognition an d machine translation\, scant lexical resources\, non-standardized orthogr aphy\, etc. Furthermore\, the format of the queries and the “Query-Weighte d Value” performance measure are non-standard and not previously studied i n the IR community. In this talk\, we will describe the Raytheon BBN CLIR system\, which was successful at addressing the above challenges and uniqu e characteristics of the program.
\nBiography
\nDamianos Karakos has been at Raytheon BBN f or the past nine years\, where he is currently a Senior Principal Engineer \, Research. Before that\, he was research faculty at Johns Hopkins Univer sity. He has worked on several Government projects (e.g.\, DARPA GALE\, DA RPA RATS\, IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety o f HLT-related topics (e.g.\, speech recognition\, speech activity detectio n\, keyword search\, information retrieval). He has published more than 60 peer-reviewed papers. His research interests lie at the intersection of h uman language technology and machine learning\, with an emphasis on statis tical methods. He obtained a PhD in Electrical Engineering from the Univer sity of Maryland\, College Park\, in 2002.
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Abstract
\nNatural language processin g has been revolutionized by neural networks\, which perform impressively well in applications such as machine translation and question answering. D espite their success\, neural networks still have some substantial shortco mings: Their internal workings are poorly understood\, and they are notori ously brittle\, failing on example types that are rare in their training d ata. In this talk\, I will use the unifying thread of hierarchical syntact ic structure to discuss approaches for addressing these shortcomings. Firs t\, I will argue for a new evaluation paradigm based on targeted\, hypothe sis-driven tests that better illuminate what models have learned\; using t his paradigm\, I will show that even state-of-the-art models sometimes fai l to recognize the hierarchical 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 behavioral failings can be explained through analy sis of models’ inductive biases and internal representations\, focusing on the puzzle of how neural networks represent discrete symbolic structure i n continuous vector space. I will close by showing how insights from these analyses can be used to make models more robust through approaches based on meta-learning\, structured architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Science at Johns Hopkins University. As an undergr aduate\, he studied computational linguistics at Yale. His research combin es natural language processing\, 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 lin guistic structure\, since these are two of the major components that deter mine 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-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240328T090955Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nModel robustness and spurious correlations have received increasing atten tion in the NLP community\, both in methods and evaluation. The term “spur ious correlation” is overloaded though and can refer to any undesirable sh ortcuts learned by the model\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we oft en (implicitly) assume that a spurious feature is irrelevant for predictio n. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying obj ects in an image. In contrast\, they carry important information that’s ne eded to make predictions by humans. In this talk\, we argue that it is mor e productive to characterize features in terms of their necessity and suff iciency for prediction. We then discuss the implications of this categoriz ation in representation\, learning\, and evaluation.
\nBiogr aphy
\nHe He is an Assistant Professor in the Department of Computer Science and the Center for Data Science at New York University. She obtained her PhD in Computer Science at the University of Maryland\, C ollege Park. Before joining NYU\, she spent a year at AWS AI and was a pos t-doc at Stanford University before that. She is interested in building ro bust and trustworthy NLP systems in human-centered settings. Her recent re search focus includes robust language understanding\, collaborative text g eneration\, and understanding capabilities and issues of large language mo dels.
\n DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” URL:https://www.clsp.jhu.edu/events/he-he-new-york-university/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,He\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240328T090955Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230130T120000 DTEND;TZID=America/New_York:20230130T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Fried (CMU) URL:https://www.clsp.jhu.edu/events/daniel-fried-cmu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Fried\,January END:VEVENT BEGIN:VEVENT UID:ai1ec-24239@www.clsp.jhu.edu DTSTAMP:20240328T090955Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nNon-in vasive neural interfaces have the potential to transform human-computer in teraction by providing users with low friction\, information rich\, always available inputs. Reality Labs at Meta is developing such an interface fo r the control of augmented reality devices based on electromyographic (EMG ) signals captured at the wrist. Speech and audio technologies turn out to be especially well suited to unlocking the full potential of these signal s and interactions and this talk will present several specific problems an d the speech and audio approaches that have advanced us towards this ultim ate goal of effortless and joyful interfaces. We will provide the necessar y neuroscientific background to understand these signals\, describe automa tic speech recognition-inspired interfaces generating text and beamforming -inspired interfaces for identifying individual neurons\, and then explain how they connect with egocentric machine intelligence tasks that might re side on these devices.
\nBiography
\nMichael I Mandel is a Research Scientist in Reality Labs at Meta. Previously\, he was an Associate Professor of Computer and Information Science at Brooklyn College and the CUNY Graduate Center working at the intersection of machi ne learning\, signal processing\, and psychoacoustics. He earned his BSc i n Computer Science from the Massachusetts Institute of Technology and his MS and PhD with distinction in Electrical Engineering from Columbia Univer sity as a Fu Foundation Presidential Scholar. He was an FQRNT Postdoctoral Research Fellow in the Machine Learning laboratory (LISA/MILA) at the Uni versité de Montréal\, an Algorithm Developer at Audience Inc\, and a Resea rch Scientist in Computer Science and Engineering at the Ohio State Univer sity. His work has been supported by the National Science Foundation\, inc luding via a CAREER award\, the Alfred P. Sloan Foundation\, and Google\, Inc.
DTSTART;TZID=America/New_York:20240129T120000 DTEND;TZID=America/New_York:20240129T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Michael I Mandel (Meta) “Speech and Audio Processing in Non-Invasiv e Brain-Computer Interfaces at Meta” URL:https://www.clsp.jhu.edu/events/michael-i-mandel-cuny/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,January\,Mandel END:VEVENT END:VCALENDAR