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Events for March 09, 2021
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CS Colloquium: Dani Yogatama (DeepMind) - Learning General Language Processing Agents
Tue, Mar 09, 2021 @ 09:00 AM - 10:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dani Yogatama, DeepMind
Talk Title: Learning General Language Processing Agents
Series: CS Colloquium
Abstract: The ability to continuously learn and generalize to new problems quickly is a hallmark of general intelligence. Existing machine learning models work well when optimized for a particular benchmark, but they require many in-domain training examples (i.e., input-output pairs that are often costly to annotate), overfit to the idiosyncrasies of the benchmark, and do not generalize to out-of-domain examples. In contrast, humans are able to accumulate task-agnostic knowledge from multiple modalities to facilitate faster learning of new skills.
In this talk, I will argue that obtaining such an ability for a language model requires significant advances in how we acquire, represent, and store knowledge in artificial systems. I will present two approaches in this direction: (i) an information theoretic framework that unifies several representation learning methods used in many domains (e.g., natural language processing, computer vision, audio processing) and allows principled constructions of new training objectives to learn better language representations; and (ii) a language model architecture that separates computation (information processing) in a large neural network and memory storage in a key-value database. I will conclude by briefly discussing a series of future research programs toward building a general linguistically intelligent agent.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dani Yogatama is a staff research scientist at DeepMind. His research interests are in machine learning and natural language processing. He received his PhD from Carnegie Mellon University in 2015. He grew up in Indonesia and was a Monbukagakusho scholar in Japan prior to studying at CMU.
Host: Xiang Ren
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Ranjay Krishna (Stanford University) - Visual Intelligence from Human Learning
Tue, Mar 09, 2021 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ranjay Krishna , Stanford University
Talk Title: Visual Intelligence from Human Learning
Series: CS Colloquium
Abstract: At the core of human development is the ability to adapt to new, previously unseen stimuli. We comprehend new situations as a composition of previously seen information and ask one another for clarification when we encounter new concepts. Yet, this ability to go beyond the confounds of their training data remains an open challenge for artificial intelligence agents. My research designs visual intelligence to reason over new compositions and acquire new concepts by interacting with people. My talk will explore these challenges and present the two following lines of work:
First, I will introduce scene graphs, a cognitively-grounded, compositional visual representation. I will discuss how to integrate scene graphs into a variety of computer vision tasks, enabling models to generalize to novel compositions from a few training examples. Since our introduction of scene graphs, the Computer Vision community has developed hundreds of scene graph models and utilized scene graphs to achieve state-of-the-art results across multiple core tasks, including object localization, captioning, image generation, question answering, 3D understanding, and spatio-temporal action recognition.
Second, I will introduce a framework for socially situated learning. This framework pushes agents beyond traditional computer vision training paradigms and enables learning from human interactions in online social environments. I will showcase a real-world deployment of our agent, which learned to acquire new visual concepts by asking people targeted questions on social media. By interacting with over 230K people over 8 months, our agent learned to recognize hundreds of new concepts. This work demonstrates the possibility for agents to adapt and self-improve in real-world social environments.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ranjay Krishna is a 5th-year Ph.D. candidate at Stanford University, where he is co-advised by Fei-Fei Li and Michael Bernstein. His research lies at the intersection of computer vision and human-computer interaction; it draws on ideas from behavioral and social sciences to improve visual intelligence. His work has been recognized by the Christofer Stephenson Memorial award, as an Accell Innovation Scholar and by two Brown Institute for Media Innovation grants. His work has also been featured in Forbes magazine and in a PBS NOVA documentary. During his Ph.D., he re-designed Stanford's undergraduate Computer Vision course and currently also instructs the graduate Computer Vision course, Stanford's second largest course. He has a M.Sc. from Stanford University. Before that, he conferred a B.Sc. with a double major in Electrical Engineering and in Computer Science from Cornell University. In the past, he has interned at Google AI, Facebook AI Research, and Yahoo Research.
Host: Ramakant Nevatia
Audiences: By invitation only.
Contact: Assistant to CS chair
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Undergraduate Advisement Drop-in HoursTue, Mar 09, 2021 @ 01:30 PM - 02:30 PM
Thomas Lord Department of Computer Science
Workshops & Infosessions
Do you have a quick question? The CS advisement team will be available for drop-in live chat advisement for declared undergraduate students in our four majors during the spring semester on Tuesdays, Wednesdays, and Thursdays from 1:30pm to 2:30pm Pacific Time. Access the live chat on our website at: https://www.cs.usc.edu/chat/
Location: Online
Audiences: Undergrad
Contact: USC Computer Science
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CS Distinguished Lecture: Jure Leskovec (Stanford University) - Mobility Networks for Modeling the Spread of COVID-19: Explaining Inequities and Informing Reopening
Tue, Mar 09, 2021 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jure Leskovec, Stanford University
Talk Title: Mobility Networks for Modeling the Spread of COVID-19: Explaining Inequities and Informing Reopening
Series: Computer Science Distinguished Lecture Series
Abstract: The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of "superspreader" POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_UD7zYBdETsCyLBOiv2DoLw
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. Dr. Leskovec was the co-founder of a machine learning startup Kosei, which was later acquired by Pinterest. His research focuses on machine learning and data mining large social, information, and biological networks. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, marketing, and biomedicine. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter at @jure.
Host: Xiang Ren
Webcast: https://usc.zoom.us/webinar/register/WN_UD7zYBdETsCyLBOiv2DoLwLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_UD7zYBdETsCyLBOiv2DoLw
Audiences: Everyone Is Invited
Contact: Computer Science Department
