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-21023@www.clsp.jhu.edu DTSTAMP:20240328T135218Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSpeech data is notoriously difficult to work with due to a variety of codecs\, lengths of recordings\, and meta-data formats. W e present Lhotse\, a speech data representation library that draws upon le ssons learned from Kaldi speech recognition toolkit and brings its concept s into the modern deep learning ecosystem. Lhotse provides a common JSON d escription format with corresponding Python classes and data preparation r ecipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and cloud storage\, la zy and on-the-fly operations amongst other features. We introduce Cut and CutSet concepts\, which simplify common data wrangling tasks for audio and help incorporate acoustic context of speech utterances. Finally\, we show how Lhotse leverages PyTorch data API abstractions and adopts them to han dle speech data for deep learning.\nBiography\nPiotr Zelasko is an assista nt research scientist in the Center for Language and Speech Processing (CL SP) who specializes in automatic speech recognition (ASR) and spoken langu age understanding (SLU). His current research focuses on applying multilin gual and crosslingual speech recognition systems to categorize the phoneti c inventory of a previously unknown language and on improving defenses aga inst adversarial attacks on both speaker identification and automatic spee ch recognition systems. He is also addressing the question of how to struc ture a spontaneous conversation into high-level semantic units such as dia log acts or topics. Finally\, he is working on Lhotse + K2\, the next-gene ration speech processing research software ecosystem. Before joining Johns Hopkins\, Zelasko worked as a machine learning consultant for Avaya (2017 -2019)\, and as a machine learning engineer for Techmo (2015-2017). Zelask o received his PhD (2019) in electronics engineering\, as well as his mast er’s (2014) and undergraduate degrees (2013) in acoustic engineering from AGH University of Science and Technology in Kraków\, Poland. DTSTART;TZID=America/New_York:20211029T120000 DTEND;TZID=America/New_York:20211029T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore MD 21218 SEQUENCE:0 SUMMARY:Piotr Zelasko (CLSP at JHU) “Lhotse: a speech data representation l ibrary for the modern deep learning ecosystem” URL:https://www.clsp.jhu.edu/events/piotr-zelasko-clsp-at-jhu-lhotse-a-spee ch-data-representation-library-for-the-modern-deep-learning-ecosystem/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nSpeech data is notoriously difficult t o work with due to a variety of codecs\, lengths of recordings\, and meta- data formats. We present Lhotse\, a speech data representation library tha t draws upon lessons learned from Kaldi speech recognition toolkit and bri ngs its concepts into the modern deep learning ecosystem. Lhotse provides a common JSON description format with corresponding Python classes and dat a preparation recipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and clo ud storage\, lazy and on-the-fly operations amongst other features. We int roduce Cut and CutSet concepts\, which simplify common data wrangling task s for audio and help incorporate acoustic context of speech utterances. Fi nally\, we show how Lhotse leverages PyTorch data API abstractions and ado pts them to handle speech data for deep learning.
\nB iography
\nPiotr Zelasko is an assistant research scientist in the Center for Language and Speech Processing (CLSP) who specializes i n automatic speech recognition (ASR) and spoken language understanding (SL U). His current research focuses on applying multilingual and crosslingual speech recognition systems to categorize the phonetic inventory of a prev iously unknown language and on improving defenses against adversarial atta cks on both speaker identification and automatic speech recognition system s. He is also addressing the question of how to structure a spontaneous co nversation into high-level semantic units such as dialog acts or topics. F inally\, he is working on Lhotse + K2\, the next-generation speech process ing research software ecosystem. Before joining Johns Hopkins\, Zelasko wo rked as a machine learning consultant for Avaya (2017-2019)\, and as a mac hine learning engineer for Techmo (2015-2017). Zelasko received his PhD (2 019) in electronics engineering\, as well as his master’s (2014) and under graduate degrees (2013) in acoustic engineering from AGH University of Sci ence and Technology in Kraków\, Poland.
\n X-TAGS;LANGUAGE=en-US:2021\,October\,Zelasko END:VEVENT BEGIN:VEVENT UID:ai1ec-21068@www.clsp.jhu.edu DTSTAMP:20240328T135218Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20211203T120000 DTEND;TZID=America/New_York:20211203T131500 LOCATION:Hackerman HallB17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Eric Ringger (Zillow Group) URL:https://www.clsp.jhu.edu/events/eric-ringger-zillow-group/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2021\,December\,Ringger END:VEVENT BEGIN:VEVENT UID:ai1ec-23312@www.clsp.jhu.edu DTSTAMP:20240328T135218Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAdvanced neural language models have grown ever large r and more complex\, pushing forward the limits of language understanding and generation\, while diminishing interpretability. The black-box nature of deep neural networks blocks humans from understanding them\, as well as trusting and using them in real-world applications. This talk will introd uce interpretation techniques that bridge the gap between humans and model s for developing trustworthy natural language processing(NLP). I will firs t show how to explain black-box models and evaluate their explanations for understanding their prediction behavior. Then I will introduce how to imp rove the interpretability of neural language models by making their decisi on-making transparent and rationalized. Finally\, I will discuss how to di agnose and improve models (e.g.\, robustness) through the lens of explanat ions. I will conclude with future research directions that are centered ar ound model interpretability and committed to facilitating communications a nd interactions between intelligent machines\, system developers\, and end users for long-term trustworthy AI.\nBiography\nHanjie Chen is a Ph.D. ca ndidate in Computer Science at the University of Virginia\, advised by Pro f. Yangfeng Ji. Her research interests lie in Trustworthy AI\, Natural Lan guage Processing (NLP)\, andInterpretable Machine Learning. She develops i nterpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Ca rlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAP WIC 2021. Her work has been published at top-tier NLP/AI conferences (e.g. \, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Wom en & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (a s the primary instructor) co-designed and taught the course\, Interpretabl e Machine Learning\, and was awarded the UVA CS Outstanding Graduate Teach ing Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found athttps://www.cs.virginia .edu/~hc9mx DTSTART;TZID=America/New_York:20230313T120000 DTEND;TZID=America/New_York:20230313T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Hanjie Chen (University of Virginia) “Bridging Humans and Machines: Techniques for Trustworthy NLP” URL:https://www.clsp.jhu.edu/events/hanjie-chen-university-of-virginia/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAdvanced neural language models have grown ever large
r and more complex\, pushing forward the limits of language understanding
and generation\, while diminishing interpretability. The black-box nature
of deep neural networks blocks humans from understanding them\, as well as
trusting and using them in real-world applications. This talk will introd
uce interpretation techniques that bridge the gap between humans and model
s for developing trustworthy natural language processing
(NLP). I will first show how to explain black-box models and evalua
te their explanations for understanding their prediction behavior. Then I
will introduce how to improve the interpretability of neural language mode
ls by making their decision-making transparent and rationalized. Finally\,
I will discuss how to diagnose and improve models (e.g.\, robustness) thr
ough the lens of explanations. I will conclude with future research direct
ions that are centered around model interpretability and committed to faci
litating communications and interactions between intelligent machines\, sy
stem developers\, and end users for long-term trustworthy AI.
Hanjie Chen is a Ph.D. candidate in Compute r Science at the University of Virginia\, advised by Prof. Yangfeng Ji. He r research interests lie in Trustworthy AI\, Natural Language Processing ( NLP)\, and
\n X-TAGS;LANGUAGE=en-US:2023\,Chen\,February END:VEVENT END:VCALENDAR Interpretable Machine Learning. She dev elops interpretation techniques to explain neural language models and make their prediction behavior transparent and reliable. She is a recipient of the Carlos and Esther Farrar Fellowship and the Best Poster Award at the ACM CAPWIC 2021. Her work has been published at top-tier NLP/AI conference s (e.g.\, ACL\, AAAI\, EMNLP\, NAACL) and selected by the National Center for Women & Information Technology (NCWIT) Collegiate Award Finalist 2021. She (as the primary instructor) co-designed and taught the course\, Inter pretable Machine Learning\, and was awarded the UVA CS Outstanding Graduat e Teaching Award and University-wide Graduate Teaching Awards Nominee (top 5% of graduate instructors). More details can be found at https://www.cs.virginia.edu/~hc9mx