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

  • Pixel Club: A Deep Learning Approach for Generic Image Segmentation

    Oran Shayer (EE, Technion)
    Sunday, 19.8.2018, 10:30
    EE Meyer Building 1061

    Recent advances in deep learning and convolutional neural networks (CNNs) have had a profound impact on almost every computer vision task. However, generic (non-semantic) image segmentation is a notable exception despite it being one of the most fundamental and widely studied tasks in this field. In this talk, we revisit the generic segmentation task and propose Deep Generic Segmentation (DGS) -- a new deep learning approach combined with conditional random fields (CRFs). Our method differs significantly from previous popular segmentation methods and consists of three stages: a new pixel-wise representation learning scheme used for generic segmentation, a segment seed generation stage, and a CRF for the final processing stage. We tested our representations and segmentation method on BSDS500 and Pascal Context. We show that we are able to learn meaningful representations for the context of segmentations and that the representations themselves achieve state-of-the-art segment similarity scores. We did not achieve optimal results on the generic segmentation task, but present promising and competitive results using this method.

    *MSc seminar under supervision of Prof. Micha Lindenbaum

  • CGGC Seminar: From Geometry to Simulation and Back: Numerical Design in Primary Manufacturing Processes

    Stefanie Elgeti (RWTH Aachen University)
    Wednesday, 5.9.2018, 13:30
    Room 337 Taub Bld.

    Using a mold or die, primary shaping manufacturing processes form material from an initially unshaped state (usually melt) into a desired shape. Examples of such a process are extrusion or high-pressure die casting — processes that are responsible for many products of our everyday life, from pipes to yoghurt cups. From the design perspective, what these processes have in common is that the exact design of the mold cannot be determined directly and intuitively from the product shape. This is due to the non-linear behavior of the material regarding the flow and solidification processes. Consequently, shape optimization as a means of numerical design can be a useful tool in mold development.

    The core of our optimization tool is the in-house flow solver XNS, which combines a space-time method with either polynomial or isogeometric shape functions with a GLS stabilization. XNS is able to exploit the common communication interfaces for distributed-memory systems. The flow solver has been coupled with the open-source optimization frameworks NLOPT and Dakota. For geometry representation, we utilize an in-house spline library which supports both NURBS and T-splines. Spline representations are very natural in engineering design, as they allow the shape optimization result to be easily transferred back into the CAD-based design process. Furthermore, they require a low number of optimization parameters and allow the incorporation of manufacturing constraints. Isogeometric analysis aligns well with this type of shape-optimization.

    Topics discussed will be our approach to shape optimization as well as methods for simulating the flow through, in and behind the mold/die. The importance of the geometrical respresentation and the resulting challenges in this area will be emphasized.

  • The 7th Summer School on Cyber and Computer Security

    The 7th Summer School on Cyber and Computer Security

    Tuesday, 2.10.2018, 09:30

    The Hiroshi Fujiwara Cyber Security Research Center will hold the 7th Summer School on Cyber and Computer Security: "Trusted Execution and Hardware Side Channels".

    The event will take place on Tuesday-Thursday, October 2nd-4th, 2018 at the Technion, Haifa.

    Mark Silberstein, Technion
    Yossi Oren, Ben-Gurion University

    Confirmed Speakers:
    Christof Fetzer, TU Dresden
    Daniel Genkin, University of Michigan
    Herbert Bos, VU Amsterdam
    Ittai Anati, Intel Israel
    Taesoo Kim, Georgia Tech

    Registration will open on 2.9.2018.

    More details and information about The Hiroshi Fujiwara Cyber Security Research Center.

  • Predicting a Better Future for Asynchronous Stochastic Gradient Decent with DANA

    Ido Hakimi, Ph.D. Thesis Seminar
    Tuesday, 30.10.2018, 14:30
    Taub 601
    Prof. Assaf Schuster

    Distributed training can significantly reduce the training time of neural networks. Despite its potential, however, distributed training has not been widely adopted due to the difficulty of scaling the training process. Existing methods suffer from slow convergence and low final accuracy when scaling to large clusters, and often require substantial re-tuning of hyper-parameters.

    We propose DANA, a novel approach that scales to large clusters while maintaining state-of-the-art accuracy and converge speed without having to re-tune parameters that are optimized for training on a single worker. By adapting Nesterov Accelerated Gradient to a distributed setting, DANA is able to predict the future position of the model's parameters and so mitigate the effect of gradient staleness, one of the main difficulties in asynchronous SGD.