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-20987@www.clsp.jhu.edu DTSTAMP:20240329T135524Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.\nBiography\nI’m a research scientist at the HLTCOE at Johns Hopk ins University. My primary research interests are in language generation\, diverse and constrained decoding\, and information retrieval. During my P hD I focused mainly on the task of text simplification\, and now am workin g on formulating structured prediction problems as end-to-end generation t asks. I received my PhD in July 2021 from the University of Pennsylvania w ith Chris Callison-Burch and Marianna Apidianaki. DTSTART;TZID=America/New_York:20211022T120000 DTEND;TZID=America/New_York:20211022T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Reno Kriz (HLTCOE – JHU) “Towards a Practically Useful Text Simplif ication System” URL:https://www.clsp.jhu.edu/events/reno-kriz-hltcoe-jhu-towards-a-practica lly-useful-text-simplification-system/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nWhile there is a vast amount of text written about ne arly any topic\, this is often difficult for someone unfamiliar with a spe cific field to understand. Automated text simplification aims to reduce th e complexity of a document\, making it more comprehensible to a broader au dience. Much of the research in this field has traditionally focused on si mplification sub-tasks\, such as lexical\, syntactic\, or sentence-level s implification. However\, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many po or transformations\; on the other hand\, recent neural models\, while prod ucing grammatical output\, often do not make all needed changes to the ori ginal text. In this thesis\, I discuss novel approaches for improving lexi cal and sentence-level simplification systems. Regarding sentence simplifi cation models\, after noting that encouraging diversity at inference time leads to significant improvements\, I take a closer look at the idea of di versity and perform an exhaustive comparison of diverse decoding technique s on other generation tasks. I also discuss the limitations in the framing of current simplification tasks\, which prevent these models from yet bei ng practically useful. Thus\, I also propose a retrieval-based reformulati on of the problem. Specifically\, starting with a document\, I identify co ncepts critical to understanding its content\, and then retrieve documents relevant for each concept\, re-ranking them based on the desired complexi ty level.
\nBiography
\nI ’m a research scientist at the HLTCOE at Johns Hopkins University. My prim ary research interests are in language generation\, diverse and constraine d decoding\, and information retrieval. During my PhD I focused mainly on the task of text simplification\, and now am working on formulating struct ured prediction problems as end-to-end generation tasks. I received my PhD in July 2021 from the University of Pennsylvania with Chris Callison-Burc h and Marianna Apidianaki.
\n\n X-TAGS;LANGUAGE=en-US:2021\,Kriz\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24157@www.clsp.jhu.edu DTSTAMP:20240329T135524Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nIn this talk\, I will present a simple extension of i mage-based Masked Autoencoders (MAE) to self-supervised representation lea rning from audio spectrograms. Following the Transformer encoder-decoder d esign in MAE\, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio\, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens\, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder\, as au dio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target dataset s. Empirically\, Audio-MAE sets new state-of-the-art performance on six au dio and speech classification tasks\, outperforming other recent models th at use external supervised pre-training.\nBio\nFlorian Metze is a Research Scientist Manager at Meta AI in New York\, supporting a team of researche rs and engineers working on multi-modal (image\, video\, audio\, text) con tent understanding for Meta’s Family of Apps (Instagram\, Threads\, Facebo ok\, WhatsApp). He used to be an Associate Research Professor at Carnegie Mellon University\, in the School of Computer Science’s Language Technolog ies Institute\, where he still is an Adjunct Professor. He is also a co-fo under of Abridge\, a company working on extracting information from doctor patient conversations. His work covers many areas of speech recognition a nd multi-media analysis with a focus on end-to-end deep learning. Currentl y\, he focuses on multi-modal processing of videos\, and using that inform ation to recommend unconnected content. In the past\, he has worked on low resource and multi-lingual speech processing\, speech recognition with ar ticulatory features\, large-scale multi-media retrieval and summarization\ , information extraction from medical interviews\, and recognition of pers onality or similar meta-data from speech.\nFor more information\, please s ee http://www.cs.cmu.edu/directory/fmetze\n DTSTART;TZID=America/New_York:20231110T120000 DTEND;TZID=America/New_York:20231110T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Florian Metze (CMU) “Masked Autoencoders that Listen” URL:https://www.clsp.jhu.edu/events/florian-metze-cmu/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nIn this talk\, I will present a simple extension of i mage-based Masked Autoencoders (MAE) to self-supervised representation lea rning from audio spectrograms. Following the Transformer encoder-decoder d esign in MAE\, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio\, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens\, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder\, as au dio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target dataset s. Empirically\, Audio-MAE sets new state-of-the-art performance on six au dio and speech classification tasks\, outperforming other recent models th at use external supervised pre-training.
\nBio
\nFlorian Metze is a Research Scientist Manager at Meta AI in New York\ , supporting a team of researchers and engineers working on multi-modal (i mage\, video\, audio\, text) content understanding for Meta’s Family of Ap ps (Instagram\, Threads\, Facebook\, WhatsApp). He used to be an Associate Research Professor at Carnegie Mellon University\, in the School of Compu ter Science’s Language Technologies Institute\, where he still is an Adjun ct Professor. He is also a co-founder of Abridge\, a company working on ex tracting information from doctor patient conversations. His work covers ma ny areas of speech recognition and multi-media analysis with a focus on en d-to-end deep learning. Currently\, he focuses on multi-modal processing o f videos\, and using that information to recommend unconnected content. In the past\, he has worked on low resource and multi-lingual speech process ing\, speech recognition with articulatory features\, large-scale multi-me dia retrieval and summarization\, information extraction from medical inte rviews\, and recognition of personality or similar meta-data from speech.< /p>\n
For more information\, please see http://www.cs.cmu.edu/directory/fmetze
\n\n X-TAGS;LANGUAGE=en-US:2023\,Metze\,November END:VEVENT END:VCALENDAR