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-20723@www.clsp.jhu.edu DTSTAMP:20240328T100015Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon.\n Biography\n\nJessy Li is an assistant professor in the Department of Lingu istics at UT Austin where she works on in computational linguistics and na tural language processing. Her work focuses on discourse processing\, text generation\, and language pragmatics in social media. She received her Ph .D. in 2017 from the University of Pennsylvania. She received an ACM SIGSO FT Distinguished Paper Award at FSE 2019\, an Area Chair Favorite at COLIN G 2018\, and a Best Paper nomination at SIGDIAL 2016.\nWeb: https://jessyl i.com DTSTART;TZID=America/New_York:20210917T120000 DTEND;TZID=America/New_York:20210917T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Jessy Li (University of Texas at Austin – Virtual Visit) “New Chall enges in Text Simplification” URL:https://www.clsp.jhu.edu/events/jessy-li-university-of-texas-at-austin/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon. p>\n
Biography
\nAbstr act
\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-23882@www.clsp.jhu.edu DTSTAMP:20240328T100015Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.\nBiograph y\nLei Li is an Assistant Professor in Language Technology Institute at Ca rnegie Mellon University. He received Ph.D. from Carnegie Mellon Universit y School of Computer Science. He is a recipient of ACL 2021 Best Paper Awa rd\, CCF Young Elite Award in 2019\, CCF distinguished speaker in 2017\, W u Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runn er-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\ , he was a faculty member at UC Santa Barbara. Prior to that\, he founded ByteDance AI Lab in 2016 and led its research in NLP\, ML\, Robotics\, an d Drug Discovery. He launched ByteDance’s machine translation system VolcT rans and AI writing system Xiaomingbot\, serving one billion users. DTSTART;TZID=America/New_York:20230901T120000 DTEND;TZID=America/New_York:20230901T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Lei Li (Carnegie Mellon University) “Empowering Responsible Use of Large Language Models” URL:https://www.clsp.jhu.edu/events/lei-li-carnegie-mellon-university-empow ering-responsible-use-of-large-language-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.
\n< strong>Biography
\nLei Li is an Assistant Professor in Lang uage Technology Institute at Carnegie Mellon University. He received Ph.D. from Carnegie Mellon University School of Computer Science. He is a recip ient of ACL 2021 Best Paper Award\, CCF Young Elite Award in 2019\, CCF di stinguished speaker in 2017\, Wu Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a faculty member at UC Santa Barba ra. Prior to that\, he founded ByteDance AI Lab in 2016 and led its resea rch in NLP\, ML\, Robotics\, and Drug Discovery. He launched ByteDance’s m achine translation system VolcTrans and AI writing system Xiaomingbot\, se rving one billion users.
\n X-TAGS;LANGUAGE=en-US:2023\,Li\,September END:VEVENT END:VCALENDAR