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:20240328T182449Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nNatural language processin g has been revolutionized by neural networks\, which perform impressively well in applications such as machine translation and question answering. D espite their success\, neural networks still have some substantial shortco mings: Their internal workings are poorly understood\, and they are notori ously brittle\, failing on example types that are rare in their training d ata. In this talk\, I will use the unifying thread of hierarchical syntact ic structure to discuss approaches for addressing these shortcomings. Firs t\, I will argue for a new evaluation paradigm based on targeted\, hypothe sis-driven tests that better illuminate what models have learned\; using t his paradigm\, I will show that even state-of-the-art models sometimes fai l to recognize the hierarchical 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 behavioral failings can be explained through analy sis of models’ inductive biases and internal representations\, focusing on the puzzle of how neural networks represent discrete symbolic structure i n continuous vector space. I will close by showing how insights from these analyses can be used to make models more robust through approaches based on meta-learning\, structured architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Science at Johns Hopkins University. As an undergr aduate\, he studied computational linguistics at Yale. His research combin es natural language processing\, 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 lin guistic structure\, since these are two of the major components that deter mine 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-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240328T182449Z 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-23586@www.clsp.jhu.edu DTSTAMP:20240328T182449Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230410T120000 DTEND;TZID=America/New_York:20230410T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Ruizhe Huang URL:https://www.clsp.jhu.edu/events/student-seminar-ruizhe-huang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Huang END:VEVENT BEGIN:VEVENT UID:ai1ec-23892@www.clsp.jhu.edu DTSTAMP:20240328T182449Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nThe growing power in compu ting and AI promises a near-term future of human-machine teamwork. In this talk\, I will present my research group’s efforts in understanding the co mplex dynamics of human-machine interaction and designing intelligent mach ines aimed to assist and collaborate with people. I will focus on 1) tools for onboarding machine teammates and authoring machine assistance\, 2) me thods for detecting\, and broadly managing\, errors in collaboration\, and 3) building blocks of knowledge needed to enable ad hoc human-machine tea mwork. I will also highlight our recent work on designing assistive\, coll aborative machines to support older adults aging in place.
\nBiography
\nChien-Ming Huang is the John C. Malone Assista nt Professor in the Department of Computer Science at the Johns Hopkins Un iversity. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier venues in HRI\, HCI \, and robotics including Science Robotics\, HRI\, CHI\, and CSCW. His res earch has received media coverage from MIT Technology Review\, Tech Inside r\, and Science Nation. Huang completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www.cs.jhu.edu/~cmhuang/
DTSTART;TZID=America/New_York:20230915T120000 DTEND;TZID=America/New_York:20230915T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Chien-Ming Huang (Johns Hopkins University) “Becoming Teammates: De signing Assistive\, Collaborative Machines” URL:https://www.clsp.jhu.edu/events/chien-ming-huang-johns-hopkins-universi ty/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24239@www.clsp.jhu.edu DTSTAMP:20240328T182449Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nNon-in vasive neural interfaces have the potential to transform human-computer in teraction by providing users with low friction\, information rich\, always available inputs. Reality Labs at Meta is developing such an interface fo r the control of augmented reality devices based on electromyographic (EMG ) signals captured at the wrist. Speech and audio technologies turn out to be especially well suited to unlocking the full potential of these signal s and interactions and this talk will present several specific problems an d the speech and audio approaches that have advanced us towards this ultim ate goal of effortless and joyful interfaces. We will provide the necessar y neuroscientific background to understand these signals\, describe automa tic speech recognition-inspired interfaces generating text and beamforming -inspired interfaces for identifying individual neurons\, and then explain how they connect with egocentric machine intelligence tasks that might re side on these devices.
\nBiography
\nMichael I Mandel is a Research Scientist in Reality Labs at Meta. Previously\, he was an Associate Professor of Computer and Information Science at Brooklyn College and the CUNY Graduate Center working at the intersection of machi ne learning\, signal processing\, and psychoacoustics. He earned his BSc i n Computer Science from the Massachusetts Institute of Technology and his MS and PhD with distinction in Electrical Engineering from Columbia Univer sity as a Fu Foundation Presidential Scholar. He was an FQRNT Postdoctoral Research Fellow in the Machine Learning laboratory (LISA/MILA) at the Uni versité de Montréal\, an Algorithm Developer at Audience Inc\, and a Resea rch Scientist in Computer Science and Engineering at the Ohio State Univer sity. His work has been supported by the National Science Foundation\, inc luding 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-TAGS;LANGUAGE=en-US:2024\,January\,Mandel END:VEVENT BEGIN:VEVENT UID:ai1ec-24479@www.clsp.jhu.edu DTSTAMP:20240328T182449Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nT he speech field is evolving to solve more challenging scenarios\, such as multi-channel recordings with multiple simultaneous talkers. Given the man y types of microphone setups out there\, we present the UniX-Encoder. It’s a universal encoder designed for multiple tasks\, and worked with any mic rophone array\, in both solo and multi-talker environments. Our research e nhances previous multichannel speech processing efforts in four key areas: 1) Adaptability: Contrasting traditional models constrained to certain mi crophone array configurations\, our encoder is universally compatible. 2) MultiTask Capability: Beyond the single-task focus of previous systems\, U niX-Encoder acts as a robust upstream model\, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervise d Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to models that first beamfor m then process single-channels\, our encoder offers an end-to-end solution \, bypassing explicit beamforming or separation. To validate its effective ness\, we tested the UniXEncoder on a synthetic multi-channel dataset from the LibriSpeech corpus. Across tasks like speech recognition and speaker diarization\, our encoder consistently outperformed combinations like the WavLM model with the BeamformIt frontend.
DTSTART;TZID=America/New_York:20240311T200500 DTEND;TZID=America/New_York:20240311T210500 SEQUENCE:0 SUMMARY:Zili Huang (JHU) “Unix-Encoder: A Universal X-Channel Speech Encode r for Ad-Hoc Microphone Array Speech Processing” URL:https://www.clsp.jhu.edu/events/zili-huang-jhu-unix-encoder-a-universal -x-channel-speech-encoder-for-ad-hoc-microphone-array-speech-processing/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,Huang\,March END:VEVENT END:VCALENDAR