קולוקוויום וסמינרים

כדי להצטרף לרשימת תפוצה של קולוקוויום מדעי המחשב, אנא בקר בדף מנויים של הרשימה.


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

Academic Calendar at Technion site.

קולוקוויום וסמינרים בקרוב

  • Pixel Club: Ultrasound Imaging with Deep Neural Networks

    דובר:
    אורטל סנוף (מדעי המחשב, טכניון)
    תאריך:
    יום שלישי, 23.10.2018, 11:30
    מקום:
    חדר 337, בניין טאוב למדעי המחשב

    Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. At the same time, there are trade-offs among different US imaging qualities such as frame-rate, resolution, signal-to-noise-ratio and contrast. So far, these trade-offs have been compensated by mostly traditional model-based signal-processing methods. In the wake of the recent revival of artificial neural networks (NN), or more specifically, deep convolutional neural networks (CNN) for different tasks including image and signal processing, we present in this work a step towards replacing the traditional ultrasound processing pipeline with a data-driven, learnable one.

    * *MSc seminar under supervision of Prof. Alex Bronstein and Prof. Michael Zibulevsky.

  • Theory Seminar: Exploring Crypto Dark Matter: New Simple PRF Candidates and Their Applications

    דובר:
    דייוויד וו (אונ'סטנפורד)
    תאריך:
    יום רביעי, 24.10.2018, 12:30
    מקום:
    טאוב 201

    Pseudorandom functions (PRFs) are one of the fundamental building blocks in cryptography. Traditionally, there have been two main approaches for PRF design: the "practitioner's approach" of building concretely-efficient constructions based on known heuristics and prior experience, and the "theoretician's approach" of proposing constructions and reducing their security to a previously-studied hardness assumption. While both approaches have their merits, the resulting PRF candidates vary greatly in terms of concrete efficiency and design complexity. In this work, we depart from these traditional approaches by exploring a new space of plausible PRF candidates. Our guiding principle is to maximize simplicity while optimizing complexity measures that are relevant to cryptographic applications. Our primary focus is on weak PRFs computable by very simple circuits--specifically, depth-2 ACC^0 circuits. Concretely, our main weak PRF candidate is a "piecewise-linear" function that first applies a secret mod-2 linear mapping to the input, and then a public mod-3 linear mapping to the result. We also put forward a similar depth-3 strong PRF candidate. The advantage of our approach is twofold. On the theoretical side, the simplicity of our candidates enables us to draw many natural connections between their hardness and questions in complexity theory or learning theory. On the applied side, the piecewise-linear structure of our candidates lends itself nicely to applications in secure multiparty computation (MPC). In this talk, I will introduce our new PRF candidates and highlight some of the connections between our candidates and questions in complexity theory, learning theory, and MPC.

    Joint work with Dan Boneh, Yuval Ishai, Alain Passelègue, and Amit Sahai.

  • Exploring the signal manifold of super-imposed pulses

    דובר:
    צ'רלס סוטון, הרצאה סמינריונית למגיסטר
    תאריך:
    יום חמישי, 25.10.2018, 14:00
    מקום:
    טאוב 401
    מנחה:
    Prof. A. Bruckstein

    Large points cloud X in $R^{n\times D}$ are often assumed to be sampled from a k-dimensional manifold where $k 1$). However, there is no evidence that this technique extends to other manifolds. This work aims to verify how the multi-scale singular value analysis of a manifold can extend to any manifold. In this work, we focus our effort on signal manifolds of super-imposed pulses (SIPS), due to their generic nature and widespread use in signal processing applications. First, we examine why the current state of the art cannot be extended to SIPS manifolds. We prove that the current approaches rely upon averaging methods that are too sensitive to the manifold’s shape. Then, we propose a method that is agnostic to the shape of manifold by utilizing the k-medoids clustering algorithm. We then present a method to tackle the problem of the estimation of the intrinsic dimensionality, including manifolds constructed out of rather noisy signals. Our method improves upon the state of the art in estimating the intrinsic dimensionality and shows promising results for an extension to any manifold.

  • CGGC Seminar: Tangent Estimation of 3D Digital Curves

    דובר:
    קספר פלוטה (מדעי המחשב, טכניון)
    תאריך:
    יום ראשון, 4.11.2018, 13:30
    מקום:
    חדר 337, בניין טאוב למדעי המחשב

    In this talk I will discuss a new tangent estimator for 3D digital curves. The proposed estimator is based on 3D digital line recognition, and it is an extension of a similar 2D tangent estimator proposed for tangent estimating along digital contours.

    The main advantages of this new tangent estimator are its speed and its asymptotic convergence – the estimated tangents converge to the ground truth as the resolution increases.

  • COLLOQUIUM LECTURE - Learning-Driven Network Protocols

    דובר:
    Michael Schapira
    תאריך:
    יום שלישי, 6.11.2018, 14:30
    מקום:
    חדר 337 טאוב.
    השתייכות:
    School of Computer Science and Engineering, Hebrew University
    מארח:
    Roy Schwartz

    Machine learning (ML) has deeply impacted many areas of computer science, including computer vision, natural language processing, computational biology, and beyond. Yet, computer networking has largely withstood the ML tide until recently. Recent advances suggest that this might be changing. We ask whether/when traditional network protocol design, which traditionally relies on the application of algorithmic insights by human experts, can be replaced by a data-driven, ML-guided approach. We will investigate this question in the context of the fundamental challenge of congestion control on the Internet. Short bio: ========== Michael Schapirs is an associate professor at the School of Computer Science and Engineering, the Hebrew University of Jerusalem. He is also the scientific co-leader of the Fraunhofer Cybersecurity Center at Hebrew University, and a member of the Center for the Study of Rationality and of the Israeli Center of Research Excellence in Algorithms. Prior to joining the Hebrew University he was a visiting scientist at Google NYC (2011/12), where he worked with the Infrastructure Networking group. He was also a postdoctoral researcher at UC Berkeley and Yale University (jointly), with Prof. Joan Feigenbaum and Prof. Scott Shenker (2008-2010), and at Princeton University, with Prof. Jennifer Rexford (2010/11). Prof. Schapira is a recipient of the Allon Fellowship (2011), the Microsoft Research Faculty Fellowship (2013), the Hebrew University President's Prize (2014), the Wolf Foundation's Krill Prize (2015), an ERC Starting Grant (2015), 2 IETF/IRTF Applied Networking Research Prizes (2014+2017), and a Google Faculty Research Award (2017). Schapira holds a B.Sc. in Mathematics and Computer Science, a B.A. in Humanities, and a Ph.D. in Computer Science, all from the Hebrew University (received in 2004, 2004, and 2008, respectively). His Ph.D. dissertation, titled ''The Economics of Internet Protocols'', was written under the supervision of Prof. Noam Nisan. During his graduate studies, he spent time at UC Berkeley and Yale University as a visiting student, interned at Microsoft Research Silicon Valley, and worked at BrightSource Industries Israel (BSII). ========================= Refreshments will be served from 14:15 Lecture starts at 14:30

  • COLLOQUIUM LECTURE - Facing Old New Frontiers in Visual Object Recognition Using Deep Learning

    דובר:
    Daphna Weinshall
    תאריך:
    יום שלישי, 13.11.2018, 14:30
    מקום:
    חדר 337 טאוב.
    השתייכות:
    School of Computer Science and Engineering, Hebrew University
    מארח:
    Roy Schwartz

    The emergence of very effective deep learning techniques in recent years has affected almost all areas of research remotely related to AI, and computer vision in particular has been changed irreversibly. In this talk I will focus on visual object recognitions. The incredible recent progress in this area, and the availability of very effective public domain tools for object recognition in images, allows us to reopen old questions and approach them from new directions with new tools. I will talk about two such questions. Specifically, in the first part of the lecture I will talk about curriculum learning, where a learner is exposed to examples whose difficulty level is gradually increased. This heuristic has been empirically shown to improve the outcome of learning in various models. Our main contribution is a theoretical result, showing that learning with a curriculum speeds up the rate of learning in the context of the regression and the hinge loss. Interestingly, we also show how curriculum learning and hard-sample mining, although conflicting at first sight, can coexist harmoniously within the same theoretical model. In the second part of the lecture I will talk about a new generative deep learning model, which we call GM-GAN. I will show how this model can be used for novelty detection, and also augment data in a semi-supervised setting when the labeled sample is small. I will conclude by showing how GM-GAN can be used for unsupervised clustering. Short Bio:
    Daphna Weinshall received the BSc degree in mathematics and computer science from Tel-Aviv University, Tel-Aviv Israel, in 1982. She received the MSc and PhD degrees in mathematics and statistics from Tel-Aviv University in 1985 and 1986, respectively, working on models of evolution and population genetics. Between 1987 and 1992, she visited the center for biological information processing at MIT and the IBM T.J. Watson Research Center. In 1993, she joined the Institute of Computer Science at the Hebrew University of Jerusalem, where she is now a full professor. Her research interests include computer and biological vision, as well as machine and human learning. Her recent interests include the learning of distance functions, object class recognition, cognitive passwords, and Virtual Reality in schizophrenia research.

  • COLLOQUIUM LECTURE - Parallelizing Inherently Sequential Computations by Breaking Dependences Precisely

    דובר:
    Madan Musuvathi
    תאריך:
    יום שלישי, 18.12.2018, 14:30
    מקום:
    חדר 337 טאוב.
    השתייכות:
    https://www.microsoft.com/en-us/research/people/madanm/
    מארח:
    Roy Schwartz
  • COLLOQUIUM LECTURE - Toward human-centered programming language design

    דובר:
    Joshua Sunshine
    תאריך:
    יום שלישי, 1.1.2019, 14:30
    מקום:
    חדר 337 טאוב.
    השתייכות:
    Institute for Software Research at Carnegie Mellon University
    מארח:
    Roy Schwartz

    Programming languages are a tool for human thought, expression, and work yet they are principally designed using mathematical and engineering techniques. In this talk, I will describe how our group has applied human-centered design techniques --- interviews, participatory design exercises, and qualitative analysis of developer forums --- in the design of three research programming systems (Plaid, Glacier, and Obsidian). I will speak frankly about the strengths and weaknesses of these approaches and discuss speculative new techniques. Short Bio: ========== Joshua Sunshine is a Systems Scientist in the Institute for Software Research at Carnegie Mellon University. He has broad research interests at the intersection of programming languages and software engineering. He is particularly interested in better understanding of the factors that influence the usability of reusable software components. He completed his Ph.D. in Software Engineering from Carnegie Mellon in December 2013. His dissertation focused on the usability of software libraries with ordering constraints (API protocols). He was advised by Jonathan Aldrich. He graduated from Brandeis University in 2004 and worked for almost four years as a software engineer before starting graduate school. ============================ Refreshments will be served from 14:15 Lecture starts at 14:30

  • Consolidating and Exploring Open Textual Knowledge

    דובר:
    Ido Dagan
    תאריך:
    יום שלישי, 15.1.2019, 14:30
    מקום:
    חדר 337 טאוב.
    השתייכות:
    Department of Computer Science, Bar Ilan University
    מארח:
    Roy Schwartz

    T B A Short Bio: ========== Ido Dagan holds B.Sc. (Summa Cum Laude) and Ph.D. degrees in Computer Science from the Technion, Israel. He conducted his Ph.D. research in collaboration with the IBM Haifa Scientific Center, where he was a research fellow in 1991. During 1992-1994 he was a Member of Technical Staff at AT&T Bell Laboratories. During 1994-1998 he has been at the Department of Computer Science of Bar Ilan University, to which he returned in 2003. During 1998-2003 he was co-founder and CTO of a text categorization startup company, FocusEngine, and VP of Technology at LingoMotors, a Cambridge Massachusetts company which acquired FocusEngine. ===================================== Refreshments will be served from 14:15 Lecture starts at 14:30