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:20240328T183401Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
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-22417@www.clsp.jhu.edu DTSTAMP:20240328T183401Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nOne of the keys to success in machine learning applications is to improve each user’s personal exper ience via personalized models. A personalized model can be a more resource -efficient solution than a general-purpose model\, too\, because it focuse s on a particular sub-problem\, for which a smaller model architecture can be good enough. However\, training a personalized model requires data fro m the particular test-time user\, which are not always available due to th eir private nature and technical challenges. Furthermore\, such data tend to be unlabeled as they can be collected only during the test time\, once after the system is deployed to user devices. One could rely on the genera lization power of a generic model\, but such a model can be too computatio nally/spatially complex for real-time processing in a resource-constrained device. In this talk\, I will present som e techniques to circumvent the lack of labeled personal data in the contex t of speech enhancement. Our machine learning models will require zero or few data samples from the test-time users\, while they can still achieve t he personalization goal. To this end\, we will investigate modularized spe ech enhancement models as well as the potential of self-supervised learnin g for personalized speech enhancement. Because our research achieves the p ersonalization goal in a data- and resource-efficient way\, it is a step t owards a more available and affordable AI for society.
\nBio graphy
\nMinje Kim is an associate professor in the Dept. of Intellig ent Systems Engineering at Indiana University\, where he leads his researc h group\, Signals and AI Group in Engineering (SAIGE). He is also an Amazo n Visiting Academic\, consulting for Amazon Lab126. At IU\, he is affiliat ed with various programs and labs such as Data Science\, Cognitive Science \, Dept. of Statistics\, and Center for Machine Learning. He earned his Ph .D. in the Dept. of Computer Science at the University of Illinois at Urba na-Champaign. Before joining UIUC\, He worked as a researcher at ETRI\, a national lab in Korea\, from 2006 to 2011. Before then\, he received his M aster’s and Bachelor’s degrees in the Dept. of Computer Science and Engine ering at POSTECH (Summa Cum Laude) and in the Division of Information and Computer Engineering at Ajou University (w ith honor) in 2006 and 2004\, respectively. He is a recipient of various a wards including NSF Career Award (2021)\, IU Trustees Teaching Award (2021 )\, IEEE SPS Best Paper Award (2020)\, and Google and Starkey’s grants for outstanding student papers in ICASSP 2013 and 2014\, respectively. He is an IEEE Senior Member and also a member of the IEEE Audio and Acoustic Sig nal Processing Technical Committee (2018-2023). He is serving as an Associ ate Editor for EURASIP Journal of Audio\, Speech\, and Music Processing\, and as a Consulting Associate Editor for IEEE Open Journal of Signal Proce ssing. He is also a reviewer\, program committee member\, or area chair fo r the major machine learning and signal processing. He filed more than 50 patent applications as an inventor.
DTSTART;TZID=America/New_York:20221202T120000 DTEND;TZID=America/New_York:20221202T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Minje Kim (Indiana University) “Personalized Speech Enhancement: Da ta- and Resource-Efficient Machine Learning” URL:https://www.clsp.jhu.edu/events/minje-kim-indiana-university/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,December\,Kim END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240328T183401Z 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:20240328T183401Z 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