Events for January 22, 2016
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AI Seminar
Fri, Jan 22, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yisong Yue, Cal Tech
Talk Title: A Decision Tree Framework for Data-Driven Speech Animation
Abstract: n many animation projects, the animation artist typically spends significant time animating the face, which involves many labor-intensive tasks that offer little potential for creative expression. One particularly tedious task is speech animation: animating the face to match spoken audio. Indeed, the often prohibitive cost of speech animation has limited the types of animations that are feasible, including localization to different languages.
In this talk, I will show how to view speech animation through the lens of data-driven sequence prediction. In contrast to previous sequence prediction settings, speech animation is an instance of contextual spatiotemporal sequence prediction, where the output is continuous and high-dimensional (e.g., a configuration of the lower face), and also depends on an input context (e.g., audio or phonetic input).
I will present a decision tree framework for learning to generate context-dependent spatiotemporal sequences given training data. This approach enjoys several attractive properties, including ease of training, fast performance at test time, and the ability to robustly tolerate corrupted training data using a novel latent variable approach. I will showcase this approach in a case study on speech animation, where our approach outperforms several competitive baselines in both quantitative and qualitative evaluations, and also demonstrates strong robustness to corrupted training data.
This is joint work with Taehwan Kim, Sarah Taylor, Barry-John Theobald, and Iain Matthews.
Biography: Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. fromCornell University and a B.S. from the University of Illinois at Urbana-Champaign.
Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for spatiotemporal reasoning, structured prediction, interactive learning systems, and learning with humans in the loop. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, sports analytics, policy learning in robotics, and adaptive routing & allocation problems.
Host: Ashish Vaswani
More Info: http://webcasterms1.isi.edu/mediasite/SilverlightPlayer/Default.aspx?peid=6147027e077e4570919a58730193abf91d
Location: Information Science Institute (ISI) - 11th floor large conference room
Audiences: Everyone Is Invited
Contact: Kary LAU
Event Link: http://webcasterms1.isi.edu/mediasite/SilverlightPlayer/Default.aspx?peid=6147027e077e4570919a58730193abf91d
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar-EXTRACTING USER INFORMATION FROM ONLINE SOCIAL MEDIA
Fri, Jan 22, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Jiwei Li, Stanford University
Talk Title: EXTRACTING USER INFORMATION FROM ONLINE SOCIAL MEDIA
Series: Natural Language Seminar
Abstract: The overwhelming popularity of online social media creates an unprecedented opportunity to display aspects of oneself. Inferring information about these users has the potential to benefit many downstream applications such as recommendation engines and targeted advertising. In this talk I will show how to extract important personal information such as major life events and personal attributes (e.g., gender, education, job) from social evidence such as the text produced by users and their friends and from properties of their social network. I will describe algorithms making use of a variety of frameworks, including distant supervision, and a deep learning architecture that learns user representations by integrating many heterogeneous social signals.
Biography: Jiwei Li is a PH.D. student in the computer science department at Stanford University, working with Prof. Dan Jurafsky. His research interests include discourse, language generation, and social networks, with a focus on deep learning methods. Jiwei receives his B.S. from Peking University in 2012. He was rewarded the Facebook Fellowship in 2015.
Host: Xing Shi and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=6b5348f2f8dc4a4dbb595eca444410d51dLocation: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=6b5348f2f8dc4a4dbb595eca444410d51d
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
