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-22374@www.clsp.jhu.edu DTSTAMP:20240329T011346Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nIn recent years\, the fiel d of Natural Language Processing has seen a profusion of tasks\, datasets\ , and systems that facilitate reasoning about real-world situations throug h language (e.g.\, RTE\, MNLI\, COMET). Such systems might\, for example\, be trained to consider a situation where “somebody dropped a glass on the floor\,” and conclude it is likely that “the glass shattered” as a result . In this talk\, I will discuss three pieces of work that revisit assumpti ons made by or about these systems. In the first work\, I develop a Defeas ible Inference task\, which enables a system to recognize when a prior ass umption it has made may no longer be true in light of new evidence it rece ives. The second work I will discuss revisits partial-input baselines\, wh ich have highlighted issues of spurious correlations in natural language r easoning datasets and led to unfavorable assumptions about models’ reasoni ng abilities. In particular\, I will discuss experiments that show models may still learn to reason in the presence of spurious dataset artifacts. F inally\, I will touch on work analyzing harmful assumptions made by reason ing models in the form of social stereotypes\, particularly in the case of free-form generative reasoning models.
\nBiography
\nRachel Rudinger is an Assistant Professor in the Department of Co mputer Science at the University of Maryland\, College Park. She holds joi nt appointments in the Department of Linguistics and the Institute for Adv anced Computer Studies (UMIACS). In 2019\, Rachel completed her Ph.D. in C omputer Science at Johns Hopkins University in the Center for Language and Speech Processing. From 2019-2020\, she was a Young Investigator at the A llen Institute for AI in Seattle\, and a visiting researcher at the Univer sity of Washington. Her research interests include computational semantics \, common-sense reasoning\, and issues of social bias and fairness in NLP.
DTSTART;TZID=America/New_York:20220916T120000 DTEND;TZID=America/New_York:20220916T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rachel Rudinger (University of Maryland\, College Park) “Not So Fas t!: Revisiting Assumptions in (and about) Natural Language Reasoning” URL:https://www.clsp.jhu.edu/events/rachel-rudinger-university-of-maryland- college-park-not-so-fast-revisiting-assumptions-in-and-about-natural-langu age-reasoning/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Rudinger\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-22417@www.clsp.jhu.edu DTSTAMP:20240329T011346Z 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 END:VCALENDAR