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:20240328T191928Z 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).
\nBiography
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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.
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