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-21259@www.clsp.jhu.edu DTSTAMP:20240328T120842Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\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 c andidate in the Department of Cognitive Science at Johns Hopkins Universit y. As an undergraduate\, he studied computational linguistics at Yale. His research combines natural language processing\, cognitive science\, and m achine learning to study how we can achieve robust generalization in model s of language\, as this remains one of the main areas where current AI sys tems fall short. In particular\, he focuses on inductive biases and repres entations of linguistic structure\, since these are two of the major compo nents that determine how learners generalize to novel types of input. DTSTART;TZID=America/New_York:20220131T120000 DTEND;TZID=America/New_York:20220131T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Tom McCoy (Johns Hopkins University) “Opening the Black Box of Deep Learning: Representations\, Inductive Biases\, and Robustness” URL:https://www.clsp.jhu.edu/events/tom-mccoy-johns-hopkins-university-open ing-the-black-box-of-deep-learning-representations-inductive-biases-and-ro bustness/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\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-21621@www.clsp.jhu.edu DTSTAMP:20240328T120842Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.\nBiography\nAlane Su hr is a PhD Candidate in the Department of Computer Science at Cornell Uni versity\, advised by Yoav Artzi. Her research spans natural language proc essing\, machine learning\, and computer vision\, with a focus on building systems that participate and continually learn in situated natural langua ge interactions with human users. Alane’s work has been recognized by pape r awards at ACL and NAACL\, and has been supported by fellowships and gran ts\, including an NSF Graduate Research Fellowship\, a Facebook PhD Fellow ship\, and research awards from AI2\, ParlAI\, and AWS. Alane has also co- organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane received a BS in Computer Science and Engineering as an Eminence Fellow at the Ohio State University. DTSTART;TZID=America/New_York:20220314T120000 DTEND;TZID=America/New_York:20220314T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Alane Suhr (Cornell University) “Reasoning and Learning in Interact ive Natural Language Systems” URL:https://www.clsp.jhu.edu/events/alane-suhr-cornell-university-reasoning -and-learning-in-interactive-natural-language-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.
\nBiog raphy
\nAlane Suhr is a PhD Candidate in the Department of Computer Science at Cornell University\, advised by Yoav Artzi. Her resea rch spans natural language processing\, machine learning\, and computer vi sion\, with a focus on building systems that participate and continually l earn in situated natural language interactions with human users. Alane’s w ork has been recognized by paper awards at ACL and NAACL\, and has been su pported by fellowships and grants\, including an NSF Graduate Research Fel lowship\, a Facebook PhD Fellowship\, and research awards from AI2\, ParlA I\, and AWS. Alane has also co-organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane receive d a BS in Computer Science and Engineering as an Eminence Fellow at the Oh io State University.
\n X-TAGS;LANGUAGE=en-US:2022\,March\,Suhr END:VEVENT BEGIN:VEVENT UID:ai1ec-22375@www.clsp.jhu.edu DTSTAMP:20240328T120842Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nI will present our work on data augmentation using st yle transfer as a way to improve domain adaptation in sequence labeling ta sks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transform the labelled out of domain data into something that stylistically is more closely related t o the target data. Then we can train a model on a combination of the gener ated data and the smaller amount of in domain data to improve NER predicti on performance. I will show recent empirical results on these efforts.\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUAL (Research in Text Understanding and Analysis of Language) lab. The common thread among all these research problems is t he scarcity of labeled data.\nBiography\nThamar Solorio is a Professor of Computer Science at the University of Houston (UH). She holds graduate deg rees in Computer Science from the Instituto Nacional de Astrofísica\, Ópti ca y Electrónica\, in Puebla\, Mexico. Her research interests include info rmation extraction from social media data\, enabling technology for code-s witched data\, stylistic modeling of text\, and more recently multimodal a pproaches for online content understanding. She is the director and founde r of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attribution\, and recipient of the 2014 Emerging L eader ABIE Award in Honor of Denice Denton. She is currently serving a sec ond term as an elected board member of the North American Chapter of the A ssociation of Computational Linguistics and was PC co-chair for NAACL 2019 . She recently joined the team of Editors in Chief for the ACL Rolling Rev iew (ARR) system. Her research is currently funded by the NSF and by ADOBE . DTSTART;TZID=America/New_York:20220923T120000 DTEND;TZID=America/New_York:20220923T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Thamar Solorio (University of Houston) “Style Transfer for Data Aug mentation in Sequence Labeling Tasks” URL:https://www.clsp.jhu.edu/events/thamar-solorio-university-of-houston-st yle-transfer-for-data-augmentation-in-sequence-labeling-tasks/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nI will present our work on data a ugmentation using style transfer as a way to improve domain adaptation in sequence labeling tasks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transfo rm the labelled out of domain data into something that stylistically is mo re closely related to the target data. Then we can train a model on a comb ination of the generated data and the smaller amount of in domain data to improve NER prediction performance. I will show recent empirical results o n these efforts.
\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUA L (Research in Text Understanding and Analysis of Language) lab. The commo n thread among all these research problems is the scarcity of labeled data .
\nBiography
\nThamar Solorio is a Professor of Computer Science at the Univer sity of Houston (UH). She holds graduate degrees in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include information extraction from social media data\, enabling technology for code-switched data\, stylistic model ing of text\, and more recently multimodal approaches for online content u nderstanding. She is the director and founder of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attrib ution\, and recipient of the 2014 Emerging Leader ABIE Award in Honor of D enice Denton. She is currently serving a second term as an elected board m ember of the North American Chapter of the Association of Computational Li nguistics and was PC co-chair for NAACL 2019. She recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE.
\n X-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240328T120842Z 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:20240328T120842Z 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