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:20240328T194759Z 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-21494@www.clsp.jhu.edu DTSTAMP:20240328T194759Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nAdversarial atta cks deceive neural network systems by adding carefully crafted perturbatio ns to benign signals. Being almost imperceptible to humans\, these attacks pose a severe security threat to the state-of-the-art speech and speaker recognition systems\, making it vital to propose countermeasures against t hem. In this talk\, we focus on 1) classification of a given adversarial a ttack into attack algorithm type\, threat model type\, and signal-to-adver sarial-noise ratios\, 2) developing a novel speech denoising solution to f urther improve the classification performance.
\nO ur proposed approach uses an x-vector network as a signature extractor to get embeddings\, which we call signatures. These signatures contain inform ation about the attack and can help classify different attack algorithms\, threat models\, and signal-to-adversarial-noise ratios. We demonstrate th e transferability of such signatures to other tasks. In particular\, a sig nature extractor trained to classify attacks against speaker identificatio n can also be used to classify attacks against speaker verification and sp eech recognition. We also show that signatures can be used to detect unkno wn attacks i.e. attacks not included during training. Lastly\, we propose to improve the signature extractor by making the job of the signature ext ractor easier by removing the clean signal from the adversarial example (w hich consists of clean signal+perturbation). We train our signature extrac tor using adversarial perturbation. At inference time\, we use a time-doma in denoiser to obtain adversarial perturbation from adversarial examples. Using our improved approach\, we show that common attacks in the literatur e (Fast Gradient Sign Method (FGSM)\, Projected Gradient Descent (PGD)\, C arlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We al so detect unknown attacks with an equal error rate (EER) of about 9%\, whi ch is very promising.
DTSTART;TZID=America/New_York:20220304T120000 DTEND;TZID=America/New_York:20220304T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Sonal Joshi “Classify and Detect Adversarial Atta cks Against Speaker and Speech Recognition Systems” URL:https://www.clsp.jhu.edu/events/student-seminar-sonal-joshi/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Joshi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-22400@www.clsp.jhu.edu DTSTAMP:20240328T194759Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nModern learning architectures for natural language processing have been very suc cessful in incorporating a huge amount of texts into their parameters. How ever\, by and large\, such models store and use knowledge in distributed a nd decentralized ways. This proves unreliable and makes the models ill-sui ted for knowledge-intensive tasks that require reasoning over factual info rmation in linguistic expressions. In this talk\, I will give a few examp les of exploring alternative architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by r epresenting\, storing and accessing knowledge in a dedicated memory compon ent.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Google Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collabo rators in Google Research.
\nBiography
\nFei is a research scientist at Google Research. Before that\, he was a Profess or of Computer Science at University of Southern California. His primary r esearch interests are machine learning and its application to various AI p roblems: speech and language processing\, computer vision\, robotics and r ecently weather forecast and climate modeling. He has a PhD (2007) from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
DTSTART;TZID=America/New_York:20221024T120000 DTEND;TZID=America/New_York:20221024T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fei Sha (University of Southern California) “Extracting Information from Text into Memory for Knowledge-Intensive Tasks” URL:https://www.clsp.jhu.edu/events/fei-sha-university-of-southern-californ ia/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT BEGIN:VEVENT UID:ai1ec-23302@www.clsp.jhu.edu DTSTAMP:20240328T194759Z 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:20240328T194759Z 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 END:VCALENDAR