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-21057@www.clsp.jhu.edu DTSTAMP:20240328T093233Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab. \nBiography\nHeidi is a Senior Lecturer (associate professor) in Computer Science at the University of Sheffield\, United Kingdom. Her research inte rests are on the application of AI-based voice technologies to healthcare. In particular\, the detection and monitoring of people’s physical and men tal health including verbal and non-verbal traits for expressions of emoti on\, anxiety\, depression and neurodegenerative conditions in e.g.\, thera peutic or diagnostic settings. DTSTART;TZID=America/New_York:20211119T120000 DTEND;TZID=America/New_York:20211119T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Heidi Christensen (University of Sheffield\, UK) Virtual Seminar “A utomated Processing of Pathological Speech: Recent Work and Ongoing Challe nges” URL:https://www.clsp.jhu.edu/events/heidi-christensen-university-of-sheffie ld-uk-virtual-seminar-automated-processing-of-pathological-speech-recent-w ork-and-ongoing-challenges/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab.
\nBiography
\nHeidi is a Senior Lecturer (as sociate professor) in Computer Science at the University of Sheffield\, Un ited Kingdom. Her research interests are on the application of AI-based vo ice technologies to healthcare. In particular\, the detection and monitori ng of people’s physical and mental health including verbal and non-verbal traits for expressions of emotion\, anxiety\, depression and neurodegenera tive conditions in e.g.\, therapeutic or diagnostic settings.
\n X-TAGS;LANGUAGE=en-US:2021\,Christensen\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-23304@www.clsp.jhu.edu DTSTAMP:20240328T093233Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nTransformers are essential to pretraining. As we appr oach 5 years of BERT\, the connection between attention as architecture an d transfer learning remains key to this central thread in NLP. Other archi tectures such as CNNs and RNNs have been used to replicate pretraining res ults\, but these either fail to reach the same accuracy or require supplem ental attention layers. This work revisits the semanal BERT result and con siders pretraining without attention. We consider replacing self-attention layers with recently developed approach for long-range sequence modeling and transformer architecture variants. Specifically\, inspired by recent p apers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM) and a bidirectional model architecture based on multiplicative gating. We discuss the results of th e proposed Bidirectional Gated SSM (BiGS) and present a range of analysis into its properties. Results show that architecture does seem to have a no table impact on downstream performance and a different inductive bias that is worth exploring further.\nBiography\nAlexander “Sasha” Rush is an Asso ciate Professor at Cornell Tech. His work is at the intersection of natura l language processing and generative modeling with applications in text ge neration\, efficient inference\, and controllability. He has written sever al popular open-source software projects supporting NLP research and data science\, and works part-time as a researcher at Hugging Face. He is the s ecretary of ICLR and developed software used to run virtual conferences du ring COVID. His work has received paper and demo awards at major NLP\, vis ualization\, and hardware conferences\, an NSF Career Award\, and a Sloan Fellowship. He tweets and blogs\, mostly about coding and ML\, at @srush_n lp. DTSTART;TZID=America/New_York:20230203T120000 DTEND;TZID=America/New_York:20230203T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sasha Rush (Cornell University) “Pretraining Without Attention” URL:https://www.clsp.jhu.edu/events/sasha-rush-cornell-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nTransformers are essential to pretraining. As we appr oach 5 years of BERT\, the connection between attention as architecture an d transfer learning remains key to this central thread in NLP. Other archi tectures such as CNNs and RNNs have been used to replicate pretraining res ults\, but these either fail to reach the same accuracy or require supplem ental attention layers. This work revisits the semanal BERT result and con siders pretraining without attention. We consider replacing self-attention layers with recently developed approach for long-range sequence modeling and transformer architecture variants. Specifically\, inspired by recent p apers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM) and a bidirectional model architecture based on multiplicative gating. We discuss the results of th e proposed Bidirectional Gated SSM (BiGS) and present a range of analysis into its properties. Results show that architecture does seem to have a no table impact on downstream performance and a different inductive bias that is worth exploring further.
\nBiography
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