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-21494@www.clsp.jhu.edu DTSTAMP:20240328T121235Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nAdversarial attacks deceive neural network systems by adding carefully crafted perturbations to benign signals. Being almost im perceptible 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 them. In this talk\, we focus on 1) cl assification of a given adversarial attack into attack algorithm type\, th reat model type\, and signal-to-adversarial-noise ratios\, 2) developing a novel speech denoising solution to further improve the classification per formance. \nOur proposed approach uses an x-vector network as a signature extractor to get embeddings\, which we call signatures. These signatures c ontain information about the attack and can help classify different attack algorithms\, threat models\, and signal-to-adversarial-noise ratios. We d emonstrate the transferability of such signatures to other tasks. In parti cular\, a signature extractor trained to classify attacks against speaker identification can also be used to classify attacks against speaker verifi cation and speech recognition. We also show that signatures can be used to detect unknown attacks i.e. attacks not included during training. Lastly \, we propose to improve the signature extractor by making the job of the signature extractor easier by removing the clean signal from the adversari al example (which consists of clean signal+perturbation). We train our sig nature extractor using adversarial perturbation. At inference time\, we us e a time-domain denoiser to obtain adversarial perturbation from adversari al examples. Using our improved approach\, we show that common attacks in the literature (Fast Gradient Sign Method (FGSM)\, Projected Gradient Desc ent (PGD)\, Carlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We also detect unknown attacks with an equal error rate (EER) of a bout 9%\, which 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-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nAdversarial attacks deceive neural network systems by adding carefully crafted perturbations to benign signals. Being almost imperceptible to humans\, these attacks pose a severe security thr eat to the state-of-the-art speech and speaker recognition systems\, makin g it vital to propose countermeasures against them. In this talk\, we focu s on 1) classification of a given adversarial attack into attack algorithm type\, threat model type\, and signal-to-adversarial-noise ratios\, 2) de veloping a novel speech denoising solution to further improve the classifi cation performance.
\nOur proposed approach uses a n x-vector network as a signature extractor to get embeddings\, which we c all signatures. These signatures contain information about the attack and can help classify different attack algorithms\, threat models\, and signal -to-adversarial-noise ratios. We demonstrate the transferability of such s ignatures to other tasks. In particular\, a signature extractor trained to classify attacks against speaker identification can also be used to class ify attacks against speaker verification and speech recognition. We also s how that signatures can be used to detect unknown attacks i.e. attacks not included during training. Lastly\, we propose to improve the signature e xtractor by making the job of the signature extractor easier by removing t he clean signal from the adversarial example (which consists of clean sign al+perturbation). We train our signature extractor using adversarial pertu rbation. At inference time\, we use a time-domain denoiser to obtain adver sarial perturbation from adversarial examples. Using our improved approach \, we show that common attacks in the literature (Fast Gradient Sign Metho d (FGSM)\, Projected Gradient Descent (PGD)\, Carlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We also detect unknown attacks w ith an equal error rate (EER) of about 9%\, which is very promising.
\n X-TAGS;LANGUAGE=en-US:2022\,Joshi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-22400@www.clsp.jhu.edu DTSTAMP:20240328T121235Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nModern learning architectures for natural language pr ocessing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable a nd makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expressions. In this tal k\, I will give a few examples of exploring alternative architectures to t ackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and accessing knowledge i n a dedicated memory component.\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 Goog le Research. Before that\, he was a Professor of Computer Science at Unive rsity of Southern California. His primary research interests are machine l earning and its application to various AI problems: speech and language pr ocessing\, computer vision\, robotics and recently weather forecast and cl imate modeling. He has a PhD (2007) from Computer and Information Scienc e 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-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable and makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expre ssions. In this talk\, I will give a few examples of exploring alternativ e architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and a ccessing knowledge in a dedicated memory component.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Goo gle Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collaborators in Google Research.< /p>\n
Biography
\nFei is a research scientist at Google Research. Before that\, he was a Professor of Computer Science at U niversity of Southern California. His primary research interests are machi ne learning and its application to various AI problems: speech and languag e processing\, computer vision\, robotics and recently weather forecast an d climate modeling. He has a PhD (2007) from Computer and Information Sc ience from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
\n X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT BEGIN:VEVENT UID:ai1ec-23308@www.clsp.jhu.edu DTSTAMP:20240328T121235Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nBiases in datasets\, or unintentionally introduced sp urious cues\, are a common source of misspecification in machine learning. Performant models trained on such data can gender stereotype or be brittl e under distribution shift. In this talk\, we present several results in multimodal and question answering applications studying sources of dataset bias\, and several mitigation methods. We propose approaches where known dimensions of dataset bias are explicitly factored out of a model during learning\, without needing to modify data. Finally\, we ask whether datase t biases can be attributable to annotator behavior during annotation. Draw ing inspiration from work in psychology on cognitive biases\, we show cert ain behavioral patterns are highly indicative of the creation of problemat ic (but valid) data instances in question answering. We give evidence that many existing observations around how dataset bias propagates to models c an be attributed to data samples created by annotators we identify.\nBiogr aphy\nMark Yatskar is an Assistant Professor at University of Pennsylvania in the department of Computer and Information Science. He did his PhD at University of Washington co-advised by Luke Zettlemoyer and Ali Farhadi. H e was a Young Investigator at the Allen Institute for Artificial Intellige nce for several years working with their computer vision team\, Prior. His work spans Natural Language Processing\, Computer Vision\, and Fairness i n Machine Learning. He received a Best Paper Award at EMNLP for work on ge nder bias amplification\, and his work has been featured in Wired and the New York Times. DTSTART;TZID=America/New_York:20230210T120000 DTEND;TZID=America/New_York:20230210T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Yatskar (University of Pennsylvania) “Understanding Dataset Bi ases: Behavioral Indicators During Annotation and Contrastive Mitigations” URL:https://www.clsp.jhu.edu/events/mark-yatskar-university-of-pennsylvania / X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nBiases in datasets\, or unintentionally introduced sp urious cues\, are a common source of misspecification in machine learning. Performant models trained on such data can gender stereotype or be brittl e under distribution shift. In this talk\, we present several results in multimodal and question answering applications studying sources of dataset bias\, and several mitigation methods. We propose approaches where known dimensions of dataset bias are explicitly factored out of a model during learning\, without needing to modify data. Finally\, we ask whether datase t biases can be attributable to annotator behavior during annotation. Draw ing inspiration from work in psychology on cognitive biases\, we show cert ain behavioral patterns are highly indicative of the creation of problemat ic (but valid) data instances in question answering. We give evidence that many existing observations around how dataset bias propagates to models c an be attributed to data samples created by annotators we identify.
\n< p>Biography\nMark Yatskar is an Assistan t Professor at University of Pennsylvania in the department of Computer an d Information Science. He did his PhD at University of Washington co-advis ed by Luke Zettlemoyer and Ali Farhadi. He was a Young Investigator at the Allen Institute for Artificial Intelligence for several years working wit h their computer vision team\, Prior. His work spans Natural Language Proc essing\, Computer Vision\, and Fairness in Machine Learning. He received a Best Paper Award at EMNLP for work on gender bias amplification\, and his work has been featured in Wired and the New York Times.
\n\n X-TAGS;LANGUAGE=en-US:2023\,February\,Yatskar END:VEVENT END:VCALENDAR