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-21270@www.clsp.jhu.edu DTSTAMP:20240329T090529Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:
Abstract
\nSocial media allows resear chers to track societal and cultural changes over time based on language a nalysis tools. Many of these tools rely on statistical algorithms which ne ed to be tuned to specific types of language. Recent studies have question ed the robustness of longitudinal analyses based on statistical methods du e to issues of temporal bias and semantic shift. To what extent are change s in semantics over time affecting the reliability of longitudinal analyse s? We examine this question through a case study: understanding shifts in mental health during the course of the COVID-19 pandemic. We demonstrate t hat a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and improv e predictive generalization over time. Ultimately\, we find that these ana lyses are critical to producing accurate longitudinal studies of social me dia.
DTSTART;TZID=America/New_York:20220207T120000 DTEND;TZID=America/New_York:20220207T131500 LOCATION:In Person or Virtual Option @ https://wse.zoom.us/j/96735183473 @ 234 Ames Hall\, 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health d uring the COVID-19 Pandemic” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media-a-case-s tudy-on-mental-health-during-the-covid-19-pandemic/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-24425@www.clsp.jhu.edu DTSTAMP:20240329T090529Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
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Over the past three decades\, the fields of automatic speech recogn ition (ASR) and machine translation (MT) have witnessed remarkable advance ments\, leading to exciting research directions such as speech-to-text tra nslation (ST). This talk will delve into the domain of conversational ST\, an essential facet of daily communication\, which presents unique challen ges including spontaneous informal language\, the presence of disfluencies \, high context dependence and a scarcity of ST paired data.
\nAbstract
\nWe introduce STAR (Stream Transduction with Anchor Representations)\, a novel Transformer-based mode l designed for efficient sequence-to-sequence transduction over streams. S TAR dynamically segments input streams to create compressed anchor represe ntations\, achieving nearly lossless compression (12x) in Automatic Speech Recognition (ASR) and outperforming existing methods. Moreover\, STAR dem onstrates superior segmentation and latency-quality trade-offs in simultan eous speech-to-text tasks\, optimizing latency\, memory footprint\, and qu ality.
DTSTART;TZID=America/New_York:20240219T120000 DTEND;TZID=America/New_York:20240219T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Steven Tan “Streaming Sequence Transduction through Dynamic Compres sion” URL:https://www.clsp.jhu.edu/events/steven-tan-streaming-sequence-transduct ion-through-dynamic-compression/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,February\,Tan END:VEVENT BEGIN:VEVENT UID:ai1ec-24457@www.clsp.jhu.edu DTSTAMP:20240329T090529Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public hea lth surveillance – it is perhaps more important than ever to reflect on th e broader purpose of such systems. While much focus has been on the potent ial for this technology to improve general health outcomes\, there also ex ists a significant\, but understated\, opportunity to use this technology to address health-related disparities. Accomplishing the latter depends no t only on our ability to effectively identify addressable areas of systemi c inequality and translate them into tasks that are machine learnable\, bu t also our ability to measure\, interpret\, and counteract barriers in tra ining data that may inhibit robustness to distribution shift upon deployme nt (i.e.\, new populations\, temporal dynamics). In this talk\, we will di scuss progress made along both of these dimensions. We will begin by provi ding background on the state of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discus s their implication on prevailing views regarding language model robustnes s in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemic inequality in healthcare by identifying and c haracterizing stigmatizing language in medical records.
DTSTART;TZID=America/New_York:20240226T120000 DTEND;TZID=America/New_York:20240226T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Keith Harrigian (JHU) “Fighting Bias From Bias: Robust Natural Lang uage Processing Techniques to Promote Health Equity” URL:https://www.clsp.jhu.edu/events/keith-harrigian-jhu-fighting-bias-from- bias-robust-natural-language-processing-techniques-to-promote-health-equit y/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,February\,Harrigian END:VEVENT END:VCALENDAR