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-21487@www.clsp.jhu.edu DTSTAMP:20240329T064834Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nEnormous amounts of ever-changing knowledge are avai lable online in diverse textual styles and diverse formats. Recent advance s in deep learning algorithms and large-scale datasets are spurring progre ss in many Natural Language Processing (NLP) tasks\, including question an swering. Nevertheless\, these models cannot scale up when task-annotated t raining data are scarce. This talk presents my lab’s work toward building general-purpose models in NLP and how to systematically evaluate them. Fir st\, I present a general model for two known tasks of question answering i n English and multiple languages that are robust to small domain shifts. Then\, I show a meta-training approach that can solve a variety of NLP tas ks with only using a few examples and introduce a benchmark to evaluate cr oss-task generalization. Finally\, I discuss neuro-symbolic approaches to address more complex tasks by eliciting knowledge from structured data and language models.\n\nBiography\n\nHanna Hajishirzi is an Assistant Profess or in the Paul G. Allen School of Computer Science & Engineering at the Un iversity of Washington and a Senior Research Manager at the Allen Institut e for AI. Her research spans different areas in NLP and AI\, focusing on d eveloping general-purpose machine learning algorithms that can solve many NLP tasks. Applications for these algorithms include question answering\, representation learning\, green AI\, knowledge extraction\, and conversati onal dialogue. Honors include the NSF CAREER Award\, Sloan Fellowship\, Al len Distinguished Investigator Award\, Intel rising star award\, best pape r and honorable mention awards\, and several industry research faculty awa rds. Hanna received her PhD from University of Illinois and spent a year a s a postdoc at Disney Research and CMU. DTSTART;TZID=America/New_York:20220225T120000 DTEND;TZID=America/New_York:20220225T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 SEQUENCE:0 SUMMARY:Hanna Hajishirzi (University of Washington & Allen Institute for AI ) “Toward Robust\, Knowledge-Rich NLP” URL:https://www.clsp.jhu.edu/events/hanna-hajishirzi-university-of-washingt on-allen-institute-for-ai-toward-robust-knowledge-rich-nlp/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\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.
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\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
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