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Events for March 03, 2022
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CS Colloquium: Saining Xie (Facebook AI Research (FAIR)) - Towards Scalable Representation Learning for Visual Recognition
Thu, Mar 03, 2022 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Saining Xie, Facebook AI Research (FAIR)
Talk Title: Towards Scalable Representation Learning for Visual Recognition
Series: CS Colloquium
Abstract: A powerful biological and cognitive representation is essential for humans' remarkable visual recognition abilities. Deep learning has achieved unprecedented success in a variety of domains over the last decade. One major driving force is representation learning, which is concerned with learning efficient, accurate, and robust representations from raw data that are useful for a downstream classifier or predictor. A modern deep learning system is composed of two core and often intertwined components: 1) neural network architectures and 2) representation learning algorithms. In this talk, we will present several studies in both directions. On the neural network modeling side, we will examine modern network design principles and how they affect the scaling behavior of ConvNets and recent Vision Transformers. Additionally, we will demonstrate how we can acquire a better understanding of neural network connectivity patterns through the lens of random graphs. In terms of representation learning algorithms, we will discuss our recent efforts to move beyond the traditional supervised learning paradigm and demonstrate how self-supervised visual representation learning, which does not require human annotated labels, can outperform its supervised learning counterpart across a variety of visual recognition tasks. The talk will encompass a variety of vision application domains and modalities (e.g. 2D images and 3D scenes). The goal is to show existing connections between the techniques specialized for different input modalities and provide some insights about diverse challenges that each modality presents. Finally, we will discuss several pressing challenges and opportunities that the "big model era" raises for computer vision research.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Saining Xie is a research scientist at Facebook AI Research (FAIR). He received his Ph.D. and M.S. degrees in computer science from the University of California San Diego, advised by Zhuowen Tu. Prior to that, he received his Bachelor's degree from Shanghai Jiao Tong University. He has broad research interests in deep learning and computer vision, with a focus on developing deep representation learning techniques to push the boundaries of core visual recognition. He is a recipient of the Marr Prize Honorable Mention at ICCV 2015.
Host: Ramakant Nevatia
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Lars Lindemann (University of Pennsylvania) - Safe AI-Enabled Autonomy
Thu, Mar 03, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Lars Lindemann, University of Pennsylvania
Talk Title: Safe AI-Enabled Autonomy
Series: CS Colloquium
Abstract: AI-enabled autonomous systems show great promise to enable many future technologies such as autonomous driving, intelligent transportation, and robotics. Over the past years, there has been tremendous success in the development of autonomous systems, which was especially accelerated by the computational advances in machine learning and AI. At the same time, however, new fundamental questions were raised regarding the safety and reliability of these increasingly complex systems that often operate in uncertain and dynamic environments. In this seminar, I will provide new insights and exciting opportunities to address these challenges.
In the first part of the seminar, I will present a data-driven optimization framework to learn safe control laws for dynamical systems. For most safety-critical systems such as self-driving cars, safe expert demonstrations in the form of system trajectories that show safe system behavior are readily available or can easily be collected. At the same time, accurate models of these systems can be identified from data or obtained from first order modeling principles. To learn safe control laws, I present a constrained optimization problem with constraints on the expert demonstrations and the system model. Safety guarantees are provided in terms of the density of the data and the smoothness of the system model. Two case studies on a self-driving car and a bipedal walking robot illustrate the presented method. In the past years, it was shown that neural networks are fragile and that their use in AI-enabled systems has resulted in systems taking excessive risk. The second part of the seminar is motivated by this fact and presents a data-driven verification framework to quantify and assess the risk of AI-enabled systems. I particularly show how risk measures, classically used in finance, can be used to quantify the risk of not being robust to failure, and how we can estimate this risk from data. We will compare and verify four different neural network controllers in terms of their risk for a self-driving car. I will conclude by sharing exciting research directions in this area.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Lars Lindemann is currently a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.Sc. degrees in Electrical and Information Engineering and his B.Sc. degree in Engineering Management in 2014 from the Christian-Albrechts-University (CAU), Kiel, Germany. He received his M.Sc. degree in Systems, Control and Robotics in 2016 and his Ph.D. degree in Electrical Engineering in 2020, both from KTH Royal Institute of Technology, Stockholm, Sweden. His current research interests include systems and control theory, formal methods, data-driven control, and autonomous systems. Lars received the Outstanding Student Paper Award at the 58th IEEE Conference on Decision and Control and was a Best Student Paper Award Finalist at the 2018 American Control Conference. He also received the Student Best Paper Award as a co-author at the 60th IEEE Conference on Decision and Control.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: By invitation only.
Contact: Assistant to CS chair
