You are here

Upcoming Seminars

ThursdayJan 17, 201912:15
Vision and Robotics SeminarRoom 1
Speaker:Tali Treibitz Title:Variational Plane-Sweeping for Multi-Image Alignment Under Extreme Noise and How is that Related to Underwater Robots?Abstract:opens in new windowin html    pdfopens in new window

We tackle the problem of multiple image alignment and 3D reconstruction under extreme noise.
Modern alignment schemes, based on similarity measures, feature matching and optical flow are often pairwise, or assume global alignment. Nevertheless, under extreme noise, the alignment success sharply deteriorates, since each image does not contain enough information. Yet, when multiple images are well aligned, the signal emerges from the stack.
As the problems of alignment and 3D reconstruction are coupled, we constrain the solution by taking into account only alignments that are geometrically feasible and solve for the entire stack simultaneously. The solution is formulated as a variational problem where only a single variable per pixel, the scene's distance, is solved for. Thus, the complexity of the algorithm is independent of the number of images. Our method outperforms state-of-the-art techniques, as indicated by our simulations and by experiments on real-world scenes.
And, finally- I will discuss how this algorithm is related to our current and planned work with underwater robots.

ThursdayJan 17, 201913:30
Geometric Functional Analysis and Probability SeminarRoom 155
Speaker:Eliran Subag Title:Optimization of random polynomials on the sphere in the full-RSB regimeAbstract:opens in new windowin html    pdfopens in new window

To compute the spectral norm of a p-tensor one needs to optimize a homogeneous polynomial of degree p over the sphere. When p=2 (the matrix case) it is algorithmically easy, but for p>2 it can be NP-hard. In this talk I will focus on (randomized) optimization in high-dimensions when the objective function is a linear combination of homogeneous polynomials with Gaussian coefficients. Such random functions are called spherical spin glasses in physics and have been extensively studied since the 80s. I will describe certain geometric properties of spherical spin glasses unique to the full-RSB case, and explain how they can be used to design a polynomial time algorithm that finds points within a small multiplicative error from the global maximum.

MondayJan 21, 201914:30
Foundations of Computer Science SeminarRoom 155
Speaker:Dana Drachsler-Cohen Title:Reliable and Interpretable Deep LearningAbstract:opens in new windowin html    pdfopens in new window

In this talk, I will present two novel, complementary methods for making deep learning models more reliable and interpretable.
First, I will present AI2, a technique for proving that a neural network satisfies a given property (e.g., robustness). The key idea is to leverage abstract interpretation to soundly over-approximate the network's behavior, enabling the analysis of large convolutional models, for the first time. Then, I will present DL2, a method which bridges logic and differentiable reasoning, allowing one to both pose interesting queries on deep models as well as train them to satisfy formal properties which capture domain knowledge.
Finally, I will briefly discuss few promising research directions where applications of automated reasoning, both discrete and probabilistic, can be effective in solving key challenges in systems (e.g., programmable networks) and security (e.g., differential privacy).

Bio: Dana Drachsler-Cohen is an ETH Postdoctoral Fellow at the department of Computer Science, ETH Zurich. Her research interests span automated reasoning, program synthesis and machine learning. She obtained her PhD from the Computer Science Department at the Technion in 2017.

TuesdayJan 22, 201911:15
Algebraic Geometry and Representation Theory SeminarRoom 155
Speaker:Avner Segal Title:Structure of degenerate principal series of exceptional groupsAbstract:opens in new windowin html    pdfopens in new window

The reducibility and structure of parabolic inductions is a basic problem in the representation theory of p-adic groups.  Of particular interest are principal series and degenerate principal series representations, that is parabolic induction of 1-dimensional representations of Levi subgroups.

In this talk, I will start by describing the functor of normalized induction and its left adjoint, the Jacquet functor, and by going through several examples in the group SL_4(Q_p) will describe an algorithm which can be used to determine reducibility of such representations.

This algorithm is the core of a joint project with Hezi Halawi, in which we study the structure of degenerate principal series of exceptional groups of type En (see https://arxiv.org/abs/1811.02974).

WednesdayJan 23, 201911:15
Machine Learning and Statistics SeminarRoom 1
Speaker:Amir WeissTitle:The Gaussian Maximum Likelihood Approach for Independent Component and Vector AnalysisAbstract:opens in new windowin html    pdfopens in new window

The Blind Source Separation (BSS) problem consists of retrieving signals of interest, termed the sources, from a dataset consisting of their mixtures. One of the most popular and common paradigms for solving the BSS problem is Independent Component Analysis (ICA), where the sources are assumed to be (only) mutually statistically independent random processes, and the mixtures are assumed to be linear combinations thereof, where the linear mixing operator is unknown. In this talk, we shall start with the Gaussian Maximum Likelihood (GML) approach for the semi-blind problem, in which the sources are assumed to be temporally-diverse Gaussian processes. Based on the principles of this approach, we shall then discuss two extensions. First, the noisy Gaussian ICA problem, for which two asymptotically optimal solutions, w.r.t. two different optimality criteria, will be presented. We shall see (both analytically and empirically) that these solutions possess attractive properties even for non-Gaussian mixtures. Then, we shall consider the Independent Vector Analysis (IVA) framework, which has emerged in recent years as an extension of ICA into multiple datasets of mixtures. In IVA, the sources in each set are independent, but may depend on sources in the other sets. We will show that in IVA, the GML approach leads to consistent separation regardless of the sources' distributions.

WednesdayJan 23, 201915:30
Algebraic Geometry and Representation Theory SeminarRoom A
Speaker:Pavel Etingof Title:Symmetric tensor categories in positive characteristicAbstract:opens in new windowin html    pdfopens in new windowNOTE SPECIAL DAY AND PLACE

I will talk about my joint work with Dave Benson which constructs new symmetric tensor categories in characteristic 2 arising from modular representation theory of elementary abelian 2-groups, and about its conjectural generalization to characteristic p>2. I will also discuss my work with Gelaki and Coulembier which shows that integral symmetric tensor categories in characteristic p>2 whose simple objects form a fusion category are super-Tannakian (i.e., representation categories of a supergroup scheme). 

ThursdayJan 24, 201912:15
Vision and Robotics SeminarRoom 1
Speaker:Idan Kligvasser Title:xUnit: Learning a Spatial Activation Function for Efficient Visual InferenceAbstract:opens in new windowin html    pdfopens in new window

In recent years, deep neural networks (DNNs) achieved unprecedented performance in many vision tasks. However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of parameters. To make DNNs implementable on platforms with limited resources, it is necessary to weaken the tradeoff between performance and efficiency. In this work, we propose a new activation unit, which is suitable for both high and low level vision problems. In contrast to the widespread per-pixel activation units, like ReLUs and sigmoids, our unit implements a learnable nonlinear function with spatial connections. This enables the net to capture much more complex features, thus requiring a significantly smaller number of layers in order to reach the same performance. We illustrate the effectiveness of our units through experiments with state-of-the-art nets for classification, denoising, and super resolution. With our approach, we are able to reduce the size of these models by nearly 50% without incurring any degradation in performance.

*Spotlight presentation at CVPR'18 (+ submitted extension)

A joint work with Tamar Rott Shaham and Tomer Michaeli

ThursdayJan 31, 201912:15
Vision and Robotics SeminarRoom 1
Speaker:Heli Ben-Hamu Title:Multi-chart Generative Surface Modeling Abstract:opens in new windowin html    pdfopens in new window

We introduce a 3D shape generative model based on deep neural networks. A new image-like (i.e., tensor) data representation for genus-zero 3D shapes is devised. It is based on the observation that complicated shapes can be well represented by multiple parameterizations (charts), each focusing on a different part of the shape.

The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation. The 3D shape tensor representation is based on a multichart structure that enjoys a shape covering property and scale-translation rigidity. Scale-translation rigidity facilitates high quality 3D shape learning and guarantees unique reconstruction.  The multi-chart structure uses as input adataset of 3D shapes (with arbitrary connectivity) and a sparse correspondence between them.

The output of our algorithm is a generative model that learns the shape distribution and is able to generate novel shapes, interpolate shapes, and explore the generated shape space. The effectiveness of the method is demonstrated for the task of anatomic shape generation including human body and bone (teeth) shape generation.

MondayFeb 04, 201914:30
Foundations of Computer Science SeminarRoom 155
Speaker:Eylon Yogev Title:The Power of Distributed Verifiers in Interactive Proofs Abstract:opens in new windowin html    pdfopens in new window

We explore the power of interactive proofs with a distributed verifier. In this setting, the verifier consists of n nodes and a graph G that defines their communication pattern. The prover is a single entity that communicates with all nodes by short messages. The goal is to verify that the graph G belongs to some language in a small number of rounds, and with small communication bound, i.e., the proof size.

This interactive model was introduced by Kol, Oshman and Saxena (PODC 2018) as a generalization of non-interactive distributed proofs. They demonstrated the power of interaction in this setting by constructing protocols for problems as Graph Symmetry and Graph Non-Isomorphism -- both of which require proofs of (n^2)-bits without interaction.

In this work, we provide a new general framework for distributed interactive proofs that allows one to translate standard interactive protocols (i.e., with a centralized verifier) to ones where the verifier is distributed with a proof size that depends on the computational complexity of the verification algorithm run by the centralized verifier. We show the following:

* Every (centralized) computation performed in time O(n) on a RAM can be translated into three-round distributed interactive protocol with O(log n) proof size. This implies that many graph problems for sparse graphs have succinct proofs (e.g., testing planarity).

* Every (centralized) computation implemented by either a small space or by uniform NC circuit can be translated into a distributed protocol with O(1) rounds and O(log n) bits proof size for the low space case and polylog(n) many rounds and proof size for NC.

* We show that for Graph Non-Isomorphism, one of the striking demonstrations of the power of interaction, there is a 4-round protocol with O(log n) proof size, improving upon the O(n*log n) proof size of Kol et al.

* For many problems, we show how to reduce proof size below the seemingly natural barrier of log n. By employing our RAM compiler, we get a 5-round protocol with proof size O(loglog n) for a family of problems including Fixed Automorphism, Clique and Leader Election (for the latter two problems we actually get O(1) proof size).

* Finally, we discuss how to make these proofs non-interactive {\em arguments} via random oracles.

Our compilers capture many natural problems and demonstrate the difficulty in showing lower bounds in these regimes.
Joint work with Moni Naor and Merav Parter.

TuesdayFeb 05, 201911:00
The Chaim Leib Pekeris Memorial Lecture
Speaker:Tim RoughgardenTitle:How Computer Science Informs Modern Auction DesignAbstract:opens in new windowin html    pdfopens in new windowDolfi and Lola Ebner Auditorium
Over the last twenty years, computer science has relied on concepts borrowed from game theory and economics to reason about applications ranging from internet routing to real-time auctions for online advertising. More recently, ideas have increasingly flowed in the opposite direction, with concepts and techniques from computer science beginning to influence economic theory and practice. In this lecture, Tim Roughgarden will illustrate this point with a detailed case study of the 2016-2017 Federal Communications Commission incentive auction for repurposing wireless spectrum. Computer science techniques, ranging from algorithms for NP-hard problems to nondeterministic communication complexity, have played a critical role both in the design of the reverse auction (with the government procuring existing licenses from television broadcasters) and in the analysis of the forward auction (when the procured licenses sell to the highest bidder).