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-20115@www.clsp.jhu.edu DTSTAMP:20240329T122955Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).\nBiography\nJiri Mekyska is the head of the BDALab (Brain Diseases Analysis Laboratory) at the Brno University of Technology\, wher e he leads a multidisciplinary team of researchers (signal processing engi neers\, data scientists\, neurologists\, psychologists) with a special foc us on the development of new digital endpoints and digital biomarkers enab ling to better understand\, diagnose and monitor neurodegenerative (e.g. P arkinson’s disease) and neurodevelopmental (e.g. dysgraphia) diseases. DTSTART;TZID=America/New_York:20210329T120000 DTEND;TZID=America/New_York:20210329T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Jiri Mekyska (Brno University of Technology) “Data Science in Small Medical Data Sets: From Logistic Regression Towards Logistic Regression” URL:https://www.clsp.jhu.edu/events/jiri-mekyska-brno-university-of-technol ogy/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).
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\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-22400@www.clsp.jhu.edu DTSTAMP:20240329T122955Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nModern learning architectures for natural language pr ocessing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable a nd makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expressions. In this tal k\, I will give a few examples of exploring alternative architectures to t ackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and accessing knowledge i n a dedicated memory component.\nThis talk is based on several joint works with Yury Zemlyanskiy (Google Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collabo rators in Google Research.\nBiography\nFei is a research scientist at Goog le Research. Before that\, he was a Professor of Computer Science at Unive rsity of Southern California. His primary research interests are machine l earning and its application to various AI problems: speech and language pr ocessing\, computer vision\, robotics and recently weather forecast and cl imate modeling. He has a PhD (2007) from Computer and Information Scienc e from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China). DTSTART;TZID=America/New_York:20221024T120000 DTEND;TZID=America/New_York:20221024T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fei Sha (University of Southern California) “Extracting Information from Text into Memory for Knowledge-Intensive Tasks” URL:https://www.clsp.jhu.edu/events/fei-sha-university-of-southern-californ ia/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable and makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expre ssions. In this talk\, I will give a few examples of exploring alternativ e architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and a ccessing knowledge in a dedicated memory component.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Goo gle Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collaborators in Google Research.< /p>\n
Biography
\nFei is a research scientist at Google Research. Before that\, he was a Professor of Computer Science at U niversity of Southern California. His primary research interests are machi ne learning and its application to various AI problems: speech and languag e processing\, computer vision\, robotics and recently weather forecast an d climate modeling. He has a PhD (2007) from Computer and Information Sc ience from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
\n X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240329T122955Z 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:20240329T122955Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNon-invasive neural interfaces have the potential to transform human-computer interaction by providing users with low friction\ , information rich\, always available inputs. Reality Labs at Meta is deve loping such an interface for the control of augmented reality devices base d on electromyographic (EMG) signals captured at the wrist. Speech and aud io technologies turn out to be especially well suited to unlocking the ful l potential of these signals and interactions and this talk will present s everal specific problems and the speech and audio approaches that have adv anced us towards this ultimate goal of effortless and joyful interfaces. W e will provide the necessary neuroscientific background to understand thes e signals\, describe automatic speech recognition-inspired interfaces gene rating text and beamforming-inspired interfaces for identifying individual neurons\, and then explain how they connect with egocentric machine intel ligence tasks that might reside on these devices.\nBiography\nMichael I Ma ndel is a Research Scientist in Reality Labs at Meta. Previously\, he was an Associate Professor of Computer and Information Science at Brooklyn Col lege and the CUNY Graduate Center working at the intersection of machine l earning\, signal processing\, and psychoacoustics. He earned his BSc in Co mputer Science from the Massachusetts Institute of Technology and his MS a nd PhD with distinction in Electrical Engineering from Columbia University as a Fu Foundation Presidential Scholar. He was an FQRNT Postdoctoral Res earch Fellow in the Machine Learning laboratory (LISA/MILA) at the Univers ité de Montréal\, an Algorithm Developer at Audience Inc\, and a Research Scientist in Computer Science and Engineering at the Ohio State University . His work has been supported by the National Science Foundation\, includi ng 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-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nNon-invasive neural interfaces ha ve the potential to transform human-computer interaction by providing user s with low friction\, information rich\, always available inputs. Reality Labs at Meta is developing such an interface for the control of augmented reality devices based on electromyographic (EMG) signals captured at the w rist. Speech and audio technologies turn out to be especially well suited to unlocking the full potential of these signals and interactions and this talk will present several specific problems and the speech and audio appr oaches that have advanced us towards this ultimate goal of effortless and joyful interfaces. We will provide the necessary neuroscientific backgroun d to understand these signals\, describe automatic speech recognition-insp ired interfaces generating text and beamforming-inspired interfaces for id entifying individual neurons\, and then explain how they connect with egoc entric machine intelligence tasks that might reside on these devices.
\nBiography
\nMichael I Mandel is a Research Sci entist in Reality Labs at Meta. Previously\, he was an Associate Professor of Computer and Information Science at Brooklyn College and the CUNY Grad uate Center working at the intersection of machine learning\, signal proce ssing\, and psychoacoustics. He earned his BSc in Computer Science from th e Massachusetts Institute of Technology and his MS and PhD with distinctio n in Electrical Engineering from Columbia University as a Fu Foundation Pr esidential Scholar. He was an FQRNT Postdoctoral Research Fellow in the Ma chine Learning laboratory (LISA/MILA) at the Université de Montréal\, an A lgorithm Developer at Audience Inc\, and a Research Scientist in Computer Science and Engineering at the Ohio State University. His work has been su pported by the National Science Foundation\, including via a CAREER award\ , the Alfred P. Sloan Foundation\, and Google\, Inc.
\n X-TAGS;LANGUAGE=en-US:2024\,January\,Mandel END:VEVENT END:VCALENDAR