Colloquia and Seminars

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Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.

Academic Calendar at Technion site.

Upcoming Colloquia & Seminars

  • Distributed Clique Detection in Dynamic Networks

    Speaker:
    Matthias Bonne, M.Sc. Thesis Seminar
    Date:
    Wednesday, 21.8.2019, 11:30
    Place:
    Room 601 Taub Bld.
    Advisor:
    Prof. K. Censor-Hillel

    Real-world networks are dynamic in nature -- nodes may join or leave the network at any time, and communication links may appear or disappear constantly. In this dynamic setting, we study the problem of triangle detection, where the nodes of a network need to determine, distributively, whether or not the network contains a triangle. We examine several variants of this problem, and present algorithms and impossibility results. Then, we extend our results to larger cliques.

  • The 9th Annual International TCE Conference on Autonomous Systems

    The 9th Annual International TCE Conference on Autonomous Systems

    Date:
    Wednesday, 11.9.2019, 09:00
    Place:
    CS Taub Build. Auditorium 1

    On Wednesday, September 11, TCE center will host this year the 9th annual Henry Taub International Conference on Autonomous Systems and will focus on the advent of autonomous systems including self-driving cars, automatic delivery drones, and service robots considered by many a major revolution of modern times that holds a profound impact on the economy and society.

    The event will be a unique opportunity to hear and meet international experts from the academia and the industry working on various aspects of autonomous systems ranging from sensing and machine learning to cyber security. as follows:

    Conference Chairs:
    Alex Bronstein, Computer Science Department, Technion
    Guy Gilboa, Electrical Engineerin, Technion

    More details, program and registration.
    Early bird registration is open until August 15.

  • Online Linear Models for Edge Computing

    Speaker:
    Hadar Sivan, M.Sc. Thesis Seminar
    Date:
    Wednesday, 11.9.2019, 11:30
    Place:
    Room 601 Taub Bld.
    Advisor:
    Prof. A. Schuster

    Maintaining an accurate trained model on an infinite data stream is challenging due to concept drifts that render a learned model inaccurate. Updating the model periodically can be expensive, and so traditional approaches for computationally limited devices involve a variation of online or incremental learning, which tend to be less robust. The advent of heterogeneous architectures and Internet-connected devices gives rise to a new opportunity. A weak processor can call upon a stronger processor or a cloud server to perform a complete batch training pass once a concept drift is detected -- trading power or network bandwidth for increased accuracy. We capitalize on this opportunity in two steps. We first develop a computationally efficient bound for changes in any linear model with convex, differentiable loss. We then propose a sliding window-based algorithm that uses a small number of batch model computations to maintain an accurate model of the data stream. It uses the bound to continuously evaluate the difference between the parameters of the existing model and a hypothetical optimal model, triggering computation only as needed. Empirical evaluation on real and synthetic datasets shows that our proposed algorithm adapts well to concept drifts and provides a better tradeoff between the number of model computations and model accuracy than classic concept drift detectors. When predicting changes in electricity prices, for example, we achieve 6% better accuracy than the popular EDDM, using only 20 model computations.

  • DeepRED: Deep Image Prior Powered by RED

    Speaker:
    Gary Mataev, M.Sc. Thesis Seminar
    Date:
    Monday, 16.9.2019, 11:30
    Place:
    Taub 401 Taub Bld.
    Advisor:
    Prof. M. Elad

    Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DIP and RED) can be merged into a highly effective unsupervised recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested problems.