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-22417@www.clsp.jhu.edu DTSTAMP:20240329T100155Z 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-23316@www.clsp.jhu.edu DTSTAMP:20240329T100155Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nUnderstanding the implicat ions underlying a text is critical to assessing its impact\, in particular the social dynamics that may result from a reading of the text. This requ ires endowing artificial intelligence (AI) systems with pragmatic reasonin g\, for example to correctly conclude that the statement “Epidemics and ca ses of disease in the 21st century are “staged”” relates to unfounded cons piracy theories. In this talk\, I discuss how shortcomings in the ability of current AI systems to reason about pragmatics present challenges to equ itable detection of false or harmful language. I demonstrate how these sho rtcomings can be addressed by imposing human-interpretable structure on de ep learning architectures using insights from linguistics.
\n< p> In the first part of the talk\, I descri be how adversarial text generation algorithms can be used to improve robus tness of content moderation systems. I then introduce a pragmatic formalis m for reasoning about harmful implications conveyed by social media text. I show how this pragmatic approach can be combined with generative neural language models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents. \nBiography
\nSaadia Gabriel is a PhD candidate in the Paul G. Al len School of Computer Science & Engineering at the University of Washingt on\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research revolves around natural language processing and m achine learning\, with a particular focus on building systems for understa nding how social commonsense manifests in text (i.e. how do people typical ly behave in social scenarios)\, as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also rece ived a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best pap er nomination and won a best paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Hol yoke College in Computer Science and Mathematics.
\nDTSTART;TZID=America/New_York:20230227T120000 DTEND;TZID=America/New_York:20230227T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Saadia Gabriel (University of Washington) “Socially Responsible and Factual Reasoning for Equitable AI Systems” URL:https://www.clsp.jhu.edu/events/saadia-gabriel-university-of-washington -socially-responsible-and-factual-reasoning-for-equitable-ai-systems/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,February\,Gabriel END:VEVENT END:VCALENDAR