Colloquia and Seminars

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Upcoming Colloquia & Seminars

  • Coding Theory: The Capacity of Multidimensional Permutations With Restricted Movement

    Speaker:
    Dor Elimelech (Ben-Gurion University)
    Date:
    Sunday, 19.1.2020, 14:30
    Place:
    Room 601 Taub Bld.

    The study of permutations is motivated by their application in coding for flash memories, and their relevance in different applications of networking technologies and various channels. We study the multidimensional constrained systems of $\mathbb{Z}^d$-permutations with restricted movement. During the talk, we will show a correspondence between these restricted permutations and perfect matchings. We will use the theory of perfect matchings to investigate several two-dimensional cases, for which we compute the exact capacity of the constrained system, and prove the existence of a polynomial-time algorithm for counting admissible patterns. We will prove that the capacity of $ \mathbb{Z} ^d$-permutations restricted by a set with full affine dimension depends only on the size of the set. We will use this result in order to compute the exact capacity for a class of two-dimensional constrained systems.

  • Similarity in Binary Executable

    Speaker:
    Yaniv David, Ph.D. Thesis Seminar
    Date:
    Sunday, 19.1.2020, 16:00
    Place:
    Room 601 Taub Bld.
    Advisor:
    Prof. E. Yahav

    We address the problem of binary code search in stripped executables (with no debug information). The main challenge is establishing binary code similarity even when the binary code has been compiled using different compilers, optimization levels and targeting diverse architectures. Moreover, the source code being compiled might be from another version of the software package or another implementation altogether. Overcoming these challenges, while avoiding false-positives, is invaluable to guiding other more costly tasks in the field of binary code analysis. These tasks include automated reverse engineering and vulnerability detection. We present an iterative process of explaining and addressing the different parts of the binary similarity problem. At each step, we further refine our similarity method: improving our representations for the binary code while incorporating techniques from other fields to create a measure for binary similarity between procedures. These fields include model theory, statistical frameworks, SMT solvers and deep neural networks. We tested our developed methods in real-world scenarios by applying them to find vulnerabilities using search and perform name prediction on binary procedures. We discovered 373 vulnerabilities affecting publicly available firmware, 147 of them in the latest available firmware version for the device, and successfully predicted procedure names improving on the state-of-the-art by 20% and improving by 84% over state-of-the-art neural models that do not use any static analysis.

  • Data Science & Deep Learning: Synthetic Data Generation

    Speaker:
    Roi Livni (Tel Aviv University)
    Date:
    Monday, 20.1.2020, 12:30
    Place:
    Taub 301 Taub Bld.

    The task of synthetic data generation can be, roughly, stated as follows: A learner gets to observe examples, sampled from some unknown distributions, and needs to output "synthetic" examples that "look similar". For example, think of an algorithm that receives examples of music tunes from some genre (e.g. jazz, classical, etc.) and in turn generates new synthetic pieces of music from that genre. This task has won considerable attention lately, due to the introduction of the algorithmic framework of Generative Adversarial Networks (GANs) which exhibit impressive empirical performance. From a theoretical perspective, the problem of synthetic data generation raises many interesting open problems. In fact, even to model the problem turns out to be challenging: on the one hand, the task requires us to generate examples that resemble the observed examples, but on the other hand, we are required not to copy the empirical sample but to actually generate new and unobserved examples.

    I will introduce two mathematical frameworks for the task of generating synthetic data. The first model we consider is inspired by GANs, and the learning algorithm has only indirect access to the target distribution via a discriminator. The second model, called DP–Foolability, exploits the notion of differential privacy as a criterion for "non-memorization". We characterize learnability in each of these models as well as discuss the interrelations. As an application, we prove that privately PAC learnable classes are DP-foolable. As we will discuss, this can be seen as an analog of the equivalence between uniform convergence and learnability in classical PAC learning.

    Short Bio:
    Roi is a senior lecturer at Department of Electrical Engineering at Tel Aviv University. His research focuses on Theoretical Machine Learning. He received his Ph.D. from the Center for Brain Sciences (ELSC) at The Hebrew University of Jerusalem under the supervision of Amir Globerson.

  • CGGC Seminar: Robust Shape Collection Matching and Correspondence from Shape Differences

    Speaker:
    Aharon Cohen (CS, Technion)
    Date:
    Monday, 20.1.2020, 16:00
    Place:
    Taub 401 Taub Bld.

    We propose a method to automatically match two shape collections with a similar shape space structure, e.g. two characters in similar poses, and compute the inter-maps between the collections. Given the intra-maps in each collection, we extract the corresponding shape difference operators, and use them to construct an embedding of the shape space of each collection. We then align the two shape spaces, and use the knowledge gained from the alignment to compute the inter-maps.

    Unlike existing approaches for collection alignment, our method is applicable to small and large collections alike, and requires no parameter tuning. Furthermore, unlike most approaches for non-isometric correspondence, our method uses solely the variation within the collection to extract the inter-maps, and therefore does not require landmarks, descriptors or any additional input. We demonstrate that we achieve high matching accuracy rates, and compute high quality maps on non-isometric shapes, which compare favorably with automatic state-of-the-art methods for non-isometric shape correspondence.

  • Project Fair in IoT, Software, Android Apps, AI, Cyber, Computer Security, Networks, Computer Vision and Virtual Reality

    Project Fair in IoT, Software, Android Apps, AI, Cyber, Computer Security, Networks, Computer Vision and Virtual Reality

    Date:
    Tuesday, 21.1.2020, 12:30
    Place:
    Transparent Hall, Beit Hastudent

    CS Labs: Systems and Software Development Laboratory (ICST), Cyber and Computer Security Laboratory (CYBER), The Laboratory for Computer Communication and Networking (LCCN), Geometric Image Processing Laboratory (GIP), invite you to visit the Spring Project Fair inProject Fair in IoT, Software, Android Apps, AI, Cyber, Computer Security, Networks, Computer Vision and Virtual Reality, including demos and presentations by 60 undergraduate teams who will answer your questions on their research.

    The event will be held on Tuesday, January 21
    , 2020, betwenn 12:30-14:30, in Beit Hastudent Transparen Hall.

    You are all invtied!

    The presenting projects

  • Search for Smart Evaders with UAV Swarms

    Speaker:
    Roee Francos, M.Sc. Thesis Seminar
    Date:
    Sunday, 26.1.2020, 11:00
    Place:
    Taub 401 Taub Bld.
    Advisor:
    Prof. A. Bruckstein

    Suppose that in a given planar circular region, there are some smart mobile evaders and we would like to find them using swarms of sweeping agents. A smart evader is a target that detects and responds to the motions of searchers by performing evasive maneuvers, to avoid interception. We assume various search configurations for the sweeping swarm of agents, and present guaranteed search techniques for single agent and multi agent swarms. These search procedures enable both confinement of smart evaders to their original domain as well as complete detection of all evaders by searching the entire expanding domain.

  • Theory Seminar: Strong Average-Case Circuit Lower Bounds from Non-trivial Derandomization

    Speaker:
    Lijie Chen (MIT)
    Date:
    Wednesday, 29.1.2020, 12:30
    Place:
    Taub 201 Taub Bld.

    We prove that for all constants a, NQP = NTIME[n^polylog(n)] cannot be (1/2 + 2^(-log^a n) )-approximated by 2^(log^a n)-size ACC^0 circuits. Previously, it was even open whether E^NP can be (1/2+1/sqrt(n))-approximated by AC^0[2] circuits. As a straightforward application, we obtain an infinitely often non-deterministic pseudorandom generator for poly-size ACC^0 circuits with seed length 2^{log^eps n}, for all eps > 0.

    More generally, we establish a connection showing that, for a typical circuit class C, non-trivial nondeterministic CAPP algorithms imply strong (1/2 + 1/n^{omega(1)}) average-case lower bounds for nondeterministic time classes against C circuits. The existence of such (deterministic) algorithms is much weaker than the widely believed conjecture PromiseBPP = PromiseP.

    Our new results build on a line of recent works, including [Murray and Williams, STOC 2018], [Chen and Williams, CCC 2019], and [Chen, FOCS 2019]. In particular, it strengthens the corresponding (1/2 + 1/polylog(n))-inapproximability average-case lower bounds in [Chen, FOCS 2019]. The two important technical ingredients are techniques from Cryptography in NC^0 [Applebaum et al., SICOMP 2006], and Probabilistic Checkable Proofs of Proximity with NC^1-computable proofs.

    This is joint work with Hanlin Ren from Tsinghua University.

  • Pixel Club:Learning-Based Strong Solutions to Forward and Inverse Problems in Partial Differential Equations

    Speaker:
    Lea Bar (Tel Aviv University)
    Date:
    Tuesday, 18.2.2020, 11:30
    Place:
    Electrical Eng. Building 1061

    We introduce a novel neural network-based partial differential equations solver for forward and inverse problems. The solver is grid free, mesh free and shape free, and the solution is approximated by a neural network.

    We employ an unsupervised approach such that the input to the network is a points set in an arbitrary domain, and the output is the set of the corresponding function values. The network is trained to minimize deviations of the learned function from the PDE solution and satisfy the boundary conditions.

    The resulting solution in turn is an explicit smooth differentiable function with a known analytical form.

    Unlike other numerical methods such as finite differences and finite elements, the derivatives of the desired function can be analytically calculated to any order. This framework therefore, enables the solution of high order non-linear PDEs. The proposed algorithm is a unified formulation of both forward and inverse problems where the optimized loss function consists of few elements: fidelity terms of L2 and L infinity norms, boundary and initial conditions constraints, and additional regularizers. This setting is flexible in the sense that regularizers can be tailored to specific problems. We demonstrate our method on several free shape 2D second order systems with application to Electrical Impedance Tomography (EIT), diffusion and wave equations.

    Short bio:
    Leah Bar holds B.Sc. in Physics, M.Sc. in Bio-Medical Engineering and PhD in Electrical Engineering from Tel-Aviv University. She worked as a post-doctoral fellow in the Department of Electrical Engineering at the University of Minnesota. She is currently a senior researcher at MaxQ-AI, a medical AI start-up, and in addition a researcher at the Mathematics Department in Tel-Aviv University. Her research interest are: machine learning, image processing, computer vision and variational methods. more details» copy to my calendar

  • Hypernetworks and a New Feedback Model

    Speaker:
    Lior Wolf - COLLOQUIUM LECTURE
    Date:
    Tuesday, 31.3.2020, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    School of Computer Science, Tel-Aviv University
    Host:
    Yuval Filmus

    Hypernetworks, also known as dynamic networks, are neural networks in which the weights of at least some of the layers vary dynamically based on the input. Such networks have composite architectures in which one network predicts the weights of another network. I will briefly describe the early days of dynamic layers and present recent results from diverse domains: 3D reconstruction from a single image, image retouching, electrical circuit design, decoding block codes, graph hypernetworks for bioinformatics, and action recognition in video. Finally, I will present a new hypernetwork-based model for the role of feedback in neural computations. Short Bio: ========== Lior Wolf is a faculty member at the School of Computer Science at Tel Aviv University and a research scientist at Facebook AI Research. Before, he was a postdoc working with Prof. Poggio at CBCL, MIT. He received his PhD working with Prof. Shashua at the Hebrew U, Jerusalem. ====================================== Refreshments will be served from 14:15 Lecture starts at 14:30