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Events

Colloquia and Seminars

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Upcoming Colloquia & Seminars

  • Pixel Club: A Theoretical Analysis of Generalization in Graph Convolutional Neural Networks
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    Ron Levie (TU Berlin)
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    Tuesday, 7.12.2021, 11:30
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    Zoom Lecture: ZOOM LINK
    In recent years, the need to accommodate non-Euclidean structures in data science has brought a boom in deep learning methods on graphs, leading to many practical applications with commercial impact. In this talk we will review the mathematical foundations of the generalization capabilities of graph convolutional neuralnetworks (GNNs). We will focus mainly on spectral GNNs, where convolution is defined as element-wise multiplication in the frequency domain of the graph. In machine learningsettings where the dataset consists of signals defined on many different graphs, the trained GNN should generalize to graphs outside the training set. AGNN is called transferable if, whenever two graphs represent the sameunderlying phenomenon, the GNN has similar repercussions on both graphs.Transferability ensures that GNNs generalize if the graphs in the test setrepresent the same phenomena as the graphs in the training set. We will discussthe different approaches to mathematically model the notions of transferability,and derive corresponding transferability error bounds, proving that GNNs havegood generalization capabilities. Bio: Ron Levie received the Ph.D. degree in applied mathematics in 2018, from Tel Aviv University, Israel. During 2018-2020, he was a postdoctoral researcher with the Research Group Applied Functional Analysis, Institute of Mathematics, TU Berlin, Germany. Since 2021 he is a researcher in the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence, Department of Mathematics,LMU Munich, Germany. Since 2021, he is also a consultant at the Huawei projectRadio-Map Assisted Pathloss Prediction, at the Communications and InformationTheory Chair, TU Berlin. He won excellence awards for his MSc and PhD studies,and a Post-Doc Minerva Fellowship. He is a guest editor at Sampling Theory, Signal Processing, and Data Analysis (SaSiDa), and was a conference chair of the Online International Conference on Computational Harmonic Analysis(Online-ICCHA 2021). His current research interests are in theory of deep learning, geometric deep learning, interpretability of deep learning, deep learning in wireless communication, harmonic analysis, wavelet theory, uncertainty principles, continuous frames, and randomized methods.
  • Fast Distributed Algorithms via Sparsity Awareness
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    Dean Leitersdorf, Ph.D. Thesis Seminar
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    Tuesday, 7.12.2021, 12:30
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    Zoom Lecture: 97497750707
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    Advisor:  Prof. Keren Censor-Hillel
    We show extremely efficient distributed algorithms for sparse matrix multiplication, distance computations (e.g. All-Pairs-Shortest-Paths, APSP), and subgraph existence problems. Our work identifies core observations regarding distributed computation and uses these to simultaneously tackle a variety of problems in several theoretical, distributed models. The central theme uniting our developments is designing sparsity-aware load balancing techniques and then applying them to problems on general, non-sparse, graphs.
  • COLLOQUIUM LECTURE - Cryptography from the Hardness of Kolmogorov Complexity
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    Rafael Pass (Cornell Tech)
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    Tuesday, 7.12.2021, 14:30
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    Room 337 Taub Bld.
    Whether one-way functions (OWFs) exist is the most important outstanding problem in Cryptography. We will survey a recent thread of work (Liu-Pass, FOCS'20, Liu-Pass, STOC'21, Liu-Pass, Crypto'21) showing the equivalence of the existence of OWFs and (mild) average-case hardness of various problems related to time-bounded Kolmogorov complexity that date back to the 1960s. These results yield the first natural, and well-studied, computational problems characterizing the feasibility of the central private-key primitives and protocols in Cryptography. Based on joint works with Yanyi Liu. Short bio: Rafael Pass graduated from MIT in 2006 and has since been a faculty member in the Computer Science Department at Cornell University. In 2013, he joined the newly founded Cornell Tech campus in New York city. His research interests are in Cryptography and its connections to Complexity Theory and Game Theory. He is the recipient of the NSF Career Award in 2008, the AFOSR Young Investigator Award in 2010, the Google Faculty award in 2015 and was named a Microsoft Faculty Fellow in 2009, a Sloan Research Fellow in 2011, and a JP Morgan Faculty fellow in 2020.
  • GoToNet: Fast Monocular Scene Exposure and Exploration
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    Tom Avrech, M.Sc. Thesis Seminar
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    Sunday, 19.12.2021, 11:30
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    Zoom Lecture: 93278373918
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    Advisor:  Prof. E. Rivlin, and Dr. Chaim Baskin
    Autonomous scene exposure and exploration in localization- and communication-denied areas -- useful for finding targets in unknown scenes, mainly when direct maneuvering of the vehicle is impossible -- remains a challenging problem in computer navigation. In this work we propose a novel deep learning-based navigation approach that is able to solve this problem and demonstrate its ability in an even more complicated setup, i.e., when computational power is limited. Our method works directly with the RGB camera input, not requiring any expensive sensors, and produces two coordinates, which we call ''Goto pixel'' and ''Lookat pixel'', delineating the movement and perception directions, correspondingly. These flying-instruction pixels are optimized to expose the largest amount of currently unexplored areas. In addition, we propose a way to generate a navigation-oriented dataset, enabling efficient training of our method using RGB and depth images. Tests conducted in a simulator achieve promising results in terms of the quantity of areas unveiled and the distances to targets.
  • Pixel Club: Computational Imagingfor Sensing High-speed Phenomena
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    Mark Sheinin (Carnegie Mellon University)
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    Tuesday, 4.1.2022, 13:30
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    Zoom Lecture: 9245008892
    Despite recent advances in sensor technology, capturing high-speed video at high-spatial resolutionsremains a challenge. This is because, in a conventional camera, the available bandwidth limits either the maximum sampling frequency or thecaptured spatial resolution. In this talk, I am going to cover our recent works that use computational imaging to allow high-speed high-resolution imagingunder certain conditions. First I will describe Diffraction Line Imaging, a novel imaging principle that combines diffractive optics with 1D (line) sensorsto allow high-speed positioning of light sources (e.g., motion capture markers,car headlights) as well structured light 3D scanning with line illumination andline sensing. Second, I will present a recent work that generalizes Diffraction Line Imaging to handle a new class of scenes, resulting in new applicationdomains such as high-speed imaging for Particle Image Velocimetry and imaging combustible particles. Lastly, I will present a novel method for sensingvibrations at high speeds (up to 63kHz), for multiple scene sources a tonce, using sensors rated for only 130Hz operation. I will presentresults from our method that include capturing vibration caused by audio sources(e.g. speakers, human voice, and musical instruments) and analysing thevibration modes of a tuning fork. Bio: Mark Sheinin is a Post-doctoral Research Associate at Carnegie Mellon University's RoboticInstitute at the Illumination and Imaging Laboratory. He received his Ph.D. inElectrical Engineering from the Technion - Israel Institute of Technology in2019. His work has received a Best Student Paper Award at IEEE CVPR 2017. He is therecipient of the Porat Award for Outstanding Graduate Students, the Jacobs-QualcommFellowship in 2017, and the Jacobs Distinguished Publication Award in 2018. Hisresearch interests include computational photography and computer vision.