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2021 Nov December 2022 Jan 2023

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4	5	6	7	8	9 Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) “Zipf’s Law Suggests a Three-Pronged Approach to Inclusive Speech Recognition” 12:00 pm Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) “Zipf’s Law Suggests a Three-Pronged Approach to Inclusive Speech Recognition” @ Hackerman Hall B17 Dec 9 @ 12:00 pm – 1:15 pm Tickets Abstract Zipf’s law is commonly glossed by the aphorism “infrequent words are frequent,” but in practice, it has often meant that there are three types of words: frequent, infrequent, and out-of-vocabulary (OOV). Speech recognition solved[...]	10
11	12	13	14	15	16	17
18	19	20	21	22	23	24
25	26	27	28	29	30	31

2021 Nov December 2022 Jan 2023

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RT @adveisner: @geomblog A partial answer: release models with Data Portraits. But that only lets you check the training set for particular… 16 hours
RT @Ale_Cervone: Had a lovely time yesterday presenting some recent work from our team on #creativeAI at @jhuclsp. I was really impressed w… Mar 19
RT @nsaphra: I did this in undergrad and it was a pretty life changing experience. It's the only computer vision project I've done! Mar 17
RT @ruyimarone: @dmvaldman This doesn't tell us whether the joke appears in GPT4's training data, but my membership inference tool says tha… Mar 16
RT @AnthropicAI: We've worked hard on making Claude-Instant very responsive. Try it out via https://t.co/c5OG5RPoLP and sign up for access… Mar 16
RT @ysu_nlp: Check out Microsoft M365 Co-Pilot, which really shows the power of LLMs + Grounding! Really glad that the Semantic Machines te… Mar 16
RT @KeisukeS_: With the assistance of GPT-4, I have successfully created a Space Invaders-like game that runs smoothly in my browser! The i… Mar 16
RT @mdredze: How do you know your field is moving fast? When your talk from November 2022 needs significant changes in March 2023 because i… Mar 15
Exciting news! The Amazon and JHU #AI2AI initiative is back for round two. Together, we're working on cutting-edge… https://t.co/ro00IzF0mX Mar 15
RT @ZiangXiao: Directly using tests designed for human capabilities is an unfounded heuristic (probably a terrible one as assuming a simila… Mar 15
RT @AnthropicAI: After working for the past few moths with key partners like @NotionHQ, @Quora, and @DuckDuckGo, we’ve been able to careful… Mar 15
RT @boazbaraktcs: Impressive! Trying to get GPT3.5 (left) to prove a false statement, which it happily obliges, vs. GPT4 (right), which rec… Mar 15
RT @OpenAI: Announcing GPT-4, a large multimodal model, with our best-ever results on capabilities and alignment: https://t.co/TwLFssyALF h… Mar 14
RT @aryamccarthy: Share with undergrads: get paid to do NLP research in France for the summer! Mar 14
RT @jhuclsp: Are you an *undergrad* who's got a thing for AI? Want to get your hands dirty with some exciting AI projects? Well, you're in… Mar 14

Seminar Videos

Abstract

Unbabel is accelerating the shift towards a world without language barriers by enabling trustworthy, seamless and scalable translations between companies and their customers.
In this talk we will show how we combine different Machine Learning techniques together with a crowd of non-professional translators and achieve professional-quality translations in an unprecedented speed and scale.

Biography
João Graça is a co-founder and the CTO of Unbabel. He did his PhD in Natural Language Processing and was a Post-Doctoral Researcher at the University of Pennsylvania, where he developed a new statistical learning method that allowed for the insertion of declarative knowledge during learning, unlocking previously impossible-to-tackle problems for solving. João was also awarded a Marie Curie, Welcome II program (2011) Scholarship, which he ended up declining to pursue entrepreneurship. He is also one of the co-founders of the Lisbon Machine Learning Summer School (LxMLS).

Unbabel: How to Combine AI with the Crowd to Scale Professional-Quality Translation-Joao Graca 2018

Abstract
The human visual system is incredibly powerful. Many people have tried to create computer systems that present information visually to take advantage of that power. The potential is great – for tasks ranging from detecting patterns and outliers to quickly browsing and comparing large datasets. And yet, the number of successful visualization programs that we use today is limited. In this talk, I will discuss common problems with visualizations, and how several approaches that we have developed avoid those problems. By applying Zoomable User Interfaces, Fisheye distortion, carefully controlled animation, and working closely with users, we have created a range of applications which we have shown to have significant benefits. I will show demos from application domains including photos, trees, graphs, and even digital libraries. To build these visualizations, we have built Piccolo, a general open source toolkit available in Java and C#. It offers a hierarchical scene graph in the same style that many 3D toolkits offer – but for 2D visualization. By offering support for graphical objects, efficient rendering, animation, event handling, etc., we Piccolo can reduce the effort in building complex visual applications with minimal run-time expense. In this talk, I will also discuss Piccolo, alternative approaches to building visualizations – and the computational expense of using Piccolo.

Biography
Benjamin B. Bederson is an Associate Professor of Computer Science and director of the Human-Computer Interaction Lab at the Institute for Advanced Computer Studies at the University of Maryland, College Park. His work is on information visualization, interaction strategies, digital libraries, and accessibility issues such as voting system usability.

Making Visualization Work – Ben Bederson (University of Maryland) - 2005

Abstract
I will introduce some of our research on improving speech translation, by reducing acoustic-phonetic confusions in accented speech, named entity recognition from speech, and semantics-based translation disambiguation. Accent is a major factor in causing acoustic-phonetic confusions for spontaneous Mandarin speech. Named entity extraction is needed to facilitate the understanding of “What, Who, When, Where, Why”‚ contained in the speech. Translation disambiguation is essential for translation accuracy. More importantly, translation disambiguation with frame semantics helps decode the meaning of a spoken query even if the recognition is not perfect. We believe our approach will bring marked improvement to speech translation performances.

Reducing Confusions and Ambiguities in Speech Translation – Pascale Fung (HKUST) - 2005

November 4, 2019

Title

New Twists for New Tricks, Making Audio Deep Learning Practical

Abstract

Deep Learning has undeniably changed the way we think about audio processing during the last five years. But in the process, it has ushered a number of new practical problems that hinder wide use and adoption. In this talk I will present a collection of projects that are geared towards resolving such issues. I will cover neural network reformulations that allow us to implement audio systems that are a couple orders of magnitude more efficient in hardware; I will talk about systems that learn audio enhancement tasks without being trained on before/after examples; and I will show how we can train models to perform complex tasks such as source separation in a fully unsupervised manner without even presenting clean audio examples to them. These projects have been motivated by real-world problems where both computational power and the ability to gather good training data is limited. We show that even under these limitations we can approximately match the performance of systems that require significantly more resources and effort.

Bio

Paris Smaragdis is an associate professor at the Computer Science and the Electrical and Computer Engineering departments of the University of Illinois at Urbana-Champaign. He completed his masters, PhD, and postdoctoral studies at MIT, performing research on computational audition. In 2006 he was selected by MIT’s Technology Review as one of the year’s top young technology innovators for his work on machine listening, in 2015 he was elevated to an IEEE Fellow for contributions in audio source separation and audio processing, and during 2016-2017 he is an IEEE Signal Processing Society Distinguished Lecturer. He has authored more than 150 papers on various aspects of audio signal processing, holds more than 50 patents worldwide, and his research has been productized by multiple companies. He has previously been the chair of the LVA/ICA community, and the chair of the IEEE Machine Learning for Signal Processing Technical Committee. He is currently the chair of the IEEE Audio and Acoustics Signal Processing Technical Committee, a senior area editor of IEEE Transactions of Signal Processing, and a member of IEEE Signal Processing Society’s Board of Governors.

New Twists for New Tricks, Making Audio Deep Learning Practical -- Paris Smaragdis (UIUC)

Abstract

Despite much progress in neural models for joint vision and language understanding, current models are largely opaque and non-compositional. Many language tasks are inherently compositional, and can be solved by decomposing them into modular sub-problems. I will describe End-to End Module Networks (N2NMNs), which learn to answer questions about images by learning to decompose the question into subtasks, implemented as neural network modules. Experimental results show that N2NMNs achieve better accuracy than state-of-the-art attentional approaches, while discovering interpretable network architectures specialized for each question. I will also talk about our recent work on dense video captioning, and describe an end-to-end network that localizes activities in a long video and generates captions to describe each detected activity.

Biography

Kate Saenko is an Associate Professor of Computer Science at Boston University, director of the Computer Vision and Learning Group and co-director of the AI Research initiative at BU. Her past academic positions include: Assistant Professor at the Computer Science Department at UMass Lowell, Postdoctoral Researcher at ICSI, Visiting Scholar at UC Berkeley EECS and a Visiting Postdoctoral Fellow in the School of Engineering and Applied Science at Harvard University. Her research interests are in the broad area of Artificial Intelligence with a focus on Adaptive Machine Learning, Learning for Vision and Language Understanding, and Deep Learning.

Connecting Vision and Language End-to-End -- Kate Saenko (Boston University) - 2018

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