Correcting Flows with Marginal Matching

Flow matching models, ODE-based generative models, generate samples by gradually morphing a simple source distribution into a target distribution. In practice, these models still fall short of perfectly replicating the target distribution, mainly due to imperfections of the learned mapping. Previous work mainly focus on alleviating discretization error, which rises from sampling a continuous trajectory with a finite number of steps. In this work we ...

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Intel AI Workshop with Dana Israeli

Sunday, 17.11.2024, 18:30

Virtual

You are Invited to Intel's practical Gen AI workshop.
Registration:
...

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Studying the Cycle Complexity of DNA Synthesis

Amit Zrihan

Wednesday, 20.11.2024, 10:30

Taub 601

DNA data storage presents an efficient solution for archiving, though synthesis time and cost pose challenges.This seminar focuses on cyclic synchronized synthesis technologies like photolithography, introducing performance metrics based on synthesis cycles.We extend prior work on channel capacity, achieving higher rates and capacities through improved encoding.Additionally, we analyze cost bounds and explore alphabet sizes larger than the standard four, inspired by...

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A Framework for Automated Position-Aware Circuit Discovery

Tal Haklay

Thursday, 31.10.2024, 11:00

Taub 8

Mechanistic interpretability research seeks to explain the internal mechanisms that operate within AI models as they perform different tasks. A widely used strategy to uncover and analyze those mechanisms is by identifying circuits within the model. A circuit is a sub-graph of the model’s computational graph, believed to be critical for executing a specific task. In recent years, several methods have been proposed for automaticall...

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Generation and Application of Relational Database Embeddings

Machinery for data analysis often requires a numeric representation of the input. Towards that, a common practice is to embed components of structured data into a high-dimensional vector space. We study the embedding of the tuples of a relational database, where existing techniques are often based on optimization tasks over a collection of random walks from the database. The focus of this paper is on the recent FoRWaRD algorithm that is designed for...

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Information and Randomness in Online Learning

Idan Mehalel

Sunday, 13.10.2024, 10:30

Taub 9

Suppose that n forecasting experts are providing daily rain/no-rain predictions, and the best among them is mistaken in at most k many days. For how many days will an optimal learner allowed to observe the predictions mis-predict? This is a fundamental problem in online learning, and other important classification problems can be reduced to it. It was studied in the 90’s by Cesa-Bianchi, Freund, Helmbold, and Warmuth who gave fine-grained bounds for the case where the lea...

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Information and Randomness in Online Learning

Idan Mehalel

Sunday, 13.10.2024, 10:30

Taub 9

Suppose that n forecasting experts are providing daily rain/no-rain predictions, and the best among them is mistaken in at most k many days. For how many days will an optimal learner allowed to observe the predictions mis-predict? This is a fundamental problem in online learning, and other important classification problems can be reduced to it. It was studied in the 90’s by Cesa-Bianchi, Freund, Helmbold, and Warmuth who gave fine-grained bounds for the case where the lea...

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From Configuration-Space Clearance to Feature-Space Margin: Sample Complexity in Learning-Based Collision Detection

Motion planning is a central challenge in robotics, with learning-based approaches gaining significant attention in recent years. This thesis focuses on a specific aspect of these approaches: using machine-learning techniques, particularly Support Vector Machines (SVM), to evaluate whether robot configurations are collision-free, an operation termed "collision detection". Despite the growing popularity of these methods, there is a lack of theoretical guarantees supporting their...

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*** Important Update *** Postponement of the Ceremony Dates.

Tuesday, 24.09.2024, 19:00

Taub

According to the instructions from the Home Front Command, we regret to inform that we must postpone the excellence ceremony scheduled for Tuesday, September 24, 2024. We will provide further details later. We all hope for better and quieter days....

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Graduation Ceremony

Monday, 23.09.2024, 17:00

Lev HaCampus

Graduation Ceremony
Monday, September 23, 2024
Lev HaCampus
17:00 Technion Ceremony
19:00 Gathering and Refreshments
20:00 Ceremony Begins
Parking is available on a first-come, first-served basis.
The number of guests is limited to 4 due to space constraints....

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Combinatorial Contracts with Constraints

Gilad Shmerler

Wednesday, 18.09.2024, 14:30

The algorithmic study of the principal-agent framework is an emerging frontier for algorithmic game theory. We extend this model by incorporating knapsack constraints to capture real-world resource limitations. To address the computational challenges arising from these constraints, we develop approximation algorithms that guarantee near-optimal outcomes for both the principal and agents. Our research contributes to the understanding of contract desi...

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Hyperbolic Geometry of Google Maps

Prof. Yuliy Baryshnikov (University of Illinois at Urbana-Champaign)

Wednesday, 18.09.2024, 11:30

861, Meyer Building

Hyperbolic Geometry of Google Maps:Navigation of the Google Maps (not to confuse with *driving with* Google Maps) on smartphones is perhaps the most intuitive and efficient UI in existence, – and the reason, as I will show, is the underlying structure of the 3D hyperbolic space.Professor Yuliy Baryshnikov’s short bio is here...

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One-Message Secure Reductions

How can two parties jointly sample from a source of correlated randomness (X,Y), say two hands of cards in a poker game, without leaking any extra information? This secure sampling question is strongly motivated by the design of efficient protocols for secure computation, allowing the two parties to compute a function of their secret inputs without revealing their inputs to each other.While there has been major progress on securely sampling some useful correlations, others ...

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Verification of Robustness Properties for Neural Networks

Anan Kabaha

Wednesday, 11.09.2024, 11:30

Deep neural networks are successful in various tasks but are also susceptible to adversarial examples: malicious input perturbations designed to deceive the network. Many adversarial attacks on image classifiers involve making imperceptible changes bounded by a small ε with respect to an Lₚ norm (e.g., p = 0, 1, 2, ∞), by a small interval neighborhood, or by semantic feature perturbations, such as adjustments in brightness, translation, or rotation. To unde...

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Information Session for Students who interested in Advanced Degrees in AI and Machine Learning

Monday, 09.09.2024, 18:00

Taub

Information Session for students who interested in advanced degrees in Artificial Intelligence and Machine Learning
Whether you're contemplating how to enter the field and unsure where to start, or if you've already completed a Master's degree and are considering starting a PhD, we invite you to join us!
Date: Monday, September 9th at 6:00 PMProgram: Scientific lectures, student panel, and mingling.
...

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Enhancing Distributed Systems with In-Network Computing

The emerging paradigm of in-network computing leverages programmable network hardware, such as switches, to enhance the performance, reliability, and functionality of distributed systems. Traditionally, networks in distributed systems have been treated merely as conduits for data, with limited assumptions about their capabilities. However, by introducing higher-level abstractions that utilize the potential of programmable network devices, significant improvements in distributed...

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Variational Generative Approaches to Self-Supervised Representation Learning: From Introspective Training to Object-Centric Learning

Unsupervised latent variable models serve as highly effective tools for representing complex data such as images or videos, relevant for applications such as robotic manipulation, video generation, novelty detection, and many more. Variational Autoencoders (VAEs) provide compact latent representations with stability and efficiency. In this talk, we will explore modern VAEs that mitigate shortcomings of classical approaches such as blurry images, and can be used as a basis for s...

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Learning In Joint Input And Downstream Task Optimization

Traditional discriminative Machine-Learning approaches often formulate problems as optimization of a parameterized function mapping from a given input space to some desired output space. While this formulation is applicable to many theoretical and practical problems, it is inherently reliant on the assumption that the dataset X is constant. The world around us is abundant with scenarios where this is either not the case, or where dropping this a...

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Unleashing Asymmetry unto Metric-based Geometric Deep Learning

Thomas Dages

Sunday, 01.09.2024, 11:30

Taub 9

Metric theory offers powerful tools for analysing and processing shapes on curved manifolds, with Riemannian metrics being the most commonly used due to their simplicity and success in many applications. However, Riemannian metrics are limited by their symmetric nature, where lengths of paths are independent of traversal direction. Finsler metrics, a generalisation including asymmetric distances, provide broader tools but are rarely applied in practice, perhaps due to their the...

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Effective Automatic Deductive Reasoning Using Equality Saturation

Eytan Singher

Wednesday, 28.08.2024, 15:30

Automated deductive reasoning plays an important role in software verification, optimization, and mathematical theorem discovery. This talk explores novel applications and extensions of equality saturation, a technique for efficiently representing and manipulating large sets of equivalent expressions, to enhance automatic deductive reasoning across various domains.In this talk, we present a collection of three works:
A symbolic theory exploration system, dubbed Thes...

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An Interpretation of Spearman Correlation via k-Subset Permutations and The Edge Collector’s Problem

Oriel Limor

Tuesday, 27.08.2024, 13:00

Taub 8

This talk will cover two topics:First, we suggest a new interpretation of Spearman correlation using k-subset permutations. We characterize the distribution of the Spearman correlation of a permutation that starts with the identity permutation over n elements and is perturbed by uniformly shuffling a random subset of k indices. We present some key aspects of this distribution, including its expected value and variance for every k between 1 and n. We then generalize it to an...

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Stable Statistics, DNA, Feature Selection, and the in-between

In this talk, I will present topics from my PhD work. The talk consists of three topics:Feature selection is a core process in building machine learning models. It is often essential for optimizing performance and sometimes essential to support practical use, such as when the number of features to be measured in the execution stage is limited by hardware or other factors. We examined the procedure in challenging situations: feature selection on ...

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On Feature Extraction from MRI Data of Crohn’s Disease patients

Diagnosing Crohn's Disease (CD) typically involves examining 2D slices from magnetic resonance enterography (MRE). However, the anisotropic resolution of MRE complicates precise 3D measurements and visualization. The absence of automated 3D measurement systems further complicates assessment. Previous methods for generating isotropic volumes from anisotropic data often rely on extensive 3D data and focus solely on interslice ...

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Improving the Performance of Direct Memory Accesses, Interrupts, and Paravirtualization of High-Performance I/O

The rapid growth of data-intensive applications and high-speed I/O devices has led to increasing demands on I/O performance in both virtualized cloud environments and bare metal setups. But existing systems struggle to fully exploit the potential of modern hardware due to inefficiencies at various I/O stack layers. This thesis presents three novel techniques that optimize I/O performance across virtualized and bare metal environments: IOctopus, cint...

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A stronger bound for linear 3-LCC

Tal Yankovitz (Tel Aviv University)

Wednesday, 21.08.2024, 13:00

Amado 814

A q-locally correctable code (LCC) C:{0,1}^k->{0,1}^n is a code in which it is possible to correct every bit of a (not too) corrupted codeword by making at most q queries to the word. The cases in which q is constant are of special interest, and so are the cases that C is linear.
In a breakthrough result Kothari and Manohar (STOC 2024) showed that for linear 3-LCC n=2^Ω(k^1/8) . In this work we prove that n=2^Ω(k^1/4) . As Reed-Muller codes yield 3-...

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The annual projects fair of the Faculty of Computer Science and the Outstanding Project competition

Wednesday, 21.08.2024, 12:30

Taub lobby, floor 0

Hello everyone,
We are pleased to invite you to the annual projects fair of the Taub Faculty of Computer Science, along with the Outstanding Project competition—Wednesday, August 21, starting at 12:30 PM in the Taub Lobby - Floor 0.
Everyone is welcome to support the competing teams and be impressed by significant and creative projects.
We look forward to seeing you!...

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First Priniciple Based Geometric Deep Learning

Geometric Deep Learning attempts to apply deel learning methodlogies to domains where a grid structure doesn't exit. We advocate for using principled methods to define the primitives of these networks. As such we define networks that stem from the symmetries of geometric representations, And show how analyzing some of these primitives spectrally reveals that combining allows for SOTA performance. This is finally complemented by the introduction of a suite of identity losses tha...

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Cooperative Graph Neural Networks

Ben Finkelshtein (University of Oxford)

Tuesday, 20.08.2024, 11:30

1061, Meyer Building

Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph neural networks follow a standard message-passing paradigm: at every layer, each node state is updated based on an aggregate of messages from its neighborhood. In this work, we propose a novel framework for training graph neural networks, where every node is view...

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Registration for the 2024 Excellent Project competition is underway! The submission deadline has been extended!

Thursday, 15.08.2024, 10:50

Taub

Did you do an innovative, interesting, groundbreaking project?
You are invited to apply at the link: https://tinyurl.com/cs-projects24
Apply by August 15, 2024
The final phase will take place on August 21, 2024 - noon on Wednesday, in the format of a project fair - the participation of the contestants is mandatory. The...

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Band Night of the Faculty of Computer Science

Wednesday, 14.08.2024, 19:00

Taub Terrace 2nd floor

Invitation to Band Night
You are invited to Band Night of the Faculty of Computer Science!!Talented students and faculty members from our department will perform in a variety of musical ensembles and styles.
Join us for an unforgettable evening of live performances and amazing energy.
Wednesday, August 14th at 7:00 PM on the Taub Terrace.
There are sheltered areas, excellent air quality, so get your strings ready – entrance is free!...

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Information Design in the 21st Century

The field of information design studies strategic information revelation by a certain sender to receivers. The Bayesian persuasion model, introduced by Kamenica and Gentzkow, assumes that the sender can trustworthily commit to a randomized information revelation policy, called a signaling scheme. In contrast, the cheap talk model assumes that the sender does not have such a commitment power. The research focuses on optimizing the sender’s util...

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Doubly-Efficient Batch Verification in Statistical Zero-Knowledge

A sequence of recent works, concluding with Mu et al. (Eurocrypt, 2024) has shown that every problem $\Pi$ admitting a non-interactive statistical zero-knowledge proof (NISZK) has an efficient zero-knowledge \emph{batch verification} protocol. Namely, an NISZK protocol for proving that $x_1, \dots, x_k \in \Pi$ with communication that only scales poly-logarithmically with $k$. A caveat of this line of work is that the prover runs in exponential-time, whereas for NP problems it ...

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Time-Aware Network Telemetry in the Data Plane

Shir Landau Feibish (The Open University)

Wednesday, 07.08.2024, 11:30

Zisapel 506

Collecting network telemetry is essential for detecting problems in the network. In recent years we have seen an abundance of research on network telemetry in the data plane. Many of these solutions analyze traffic continuously over a long period of time, while resetting the structure from time to time. If shorter time intervals are needed, sliding windows are usually used, yet these incur significant resource and management overhead. However, often in order to unders...

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End Of Year Festival

Tuesday, 06.08.2024, 18:30

Taub balcony + the transparent hall in the student house

The Computer Science Student Council brings you the "The Good, the Bad and the Ugly" festivalTuesday, August 66:30 p.m. Lecturers pour beers, free food and drink, karaoke and surprises (admission for students from MDAMH only), on the Taub terrace.9:00 p.m. party in the transparent hall (with ticket presentation only)
The event is sponsored by Plus500...

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Statistically dense intervals in binary sequences with applications to assessing local enrichment in the human genome

Statistical enrichment tools are highly useful in biological research. Current approaches to statistical enrichment in ranked or ordered lists such as, for example, GSEA and GOrilla, are limited to the suffix (prefix) of the list. These methods assess extreme density of 1s in binary vectors on either side. Statistical significance can be assigned using, e.g, Wilcoxon Rank Sum and mHG statistics.In this work we extend the mHG approach to also add...

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Type Automata

Ori Roth

Tuesday, 06.08.2024, 10:00

Taub 8

Modern programming languages rely on advanced and intricate type systems.Expressive type systems support more language features but often result in unexpected language capabilities. For example, we know that the Java type system is Turing complete, which means it is so complex that Java compilers cannot guarantee termination. But a powerful type system can also be a blessing in disguise. Over the years, crafty programmers found wa...

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Object detection under the linear subspace model

Yoel Shkolnisky (Tel Aviv University)

Monday, 05.08.2024, 15:30

Amado 232

Detecting unknown objects in noisy data is a key task in many problems. A natural model for the unknown objects is the linear subspace model, which assumes that the objects can be expanded in some known basis (such as the Fourier basis). In this talk, I will present an object detection algorithm that under the linear subspace model is asymptotically guaranteed to find all objects while making only a small percentage of false discoveries. We demonstrate our derivations for the p...

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Orientation day of the Faculty of Computer Science, Technion

Friday, 02.08.2024, 10:00

Azrieli Sarona, Tel Aviv

Do you know someone who wants to do something big?
Orientation day for a bachelor's degree in the Faculty of Computer Science at the Technion is approaching and you are all invited!
Friday 2.8 | Sharona, Tel Aviv
For more details and to register: ...

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Green AI

Roy Schwartz (HUJI)

Tuesday, 30.07.2024, 14:30

Taub 337

The computations required for deep learning research have been doubling every few months, resulting in an estimated 5,000x increase from 2018 to 2022. This trend has led to unprecedented success in a range of AI tasks. In this talk I will discuss a few troubling side-effects of this trend, touching on issues of lack of inclusiveness within the research community, and an increasingly large environmental footprint. I will then present Green AI – an ...

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On the Hölder Stability of Multiset and Graph Neural Networks

Famously, multiset neural networks based on sum-pooling can separate all distinct multisets, and as a result can be used by message passing neural networks (MPNNs) to separate all pairs of graphs that can be separated by the 1-WL graph isomorphism test. However, the quality of this separation may be very weak, to the extent that the embeddings of "separable" multisets and graphs might even be considered identical when using fixed finite pre...

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The CTF Technipwn group invites you to a special guest lecture from Intel

Sunday, 28.07.2024, 18:30

Taub, 0 floor

The CTF Technipwn group invites you to a special guest lecture:RingHopper - the SMM weaknesses we found in billions of devices - a security researcher from Intel An attacker who manages to penetrate the SMM mode can bypass almost any security mechanism, steal confidential information, install malware, and even disable the entire system. In this lecture, security researcher Benny Seltzer from Intel will present a study presented at the international DEFCON conference, we wil...

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The capacity and covering depth of composite DNA

This paper studies two problems that are motivated by the novel recent approach of composite DNA that takes advantage of the DNA synthesis property which generates a huge number of copies for every synthesized strand. Under this paradigm, every composite symbols does not store a single nucleotide but a mixture of the four DNA nucleotides. In the first problem, our goal is study how to carefully choose a fixed number of mixtures of the DNA nucleotides such that the decoding prob...

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On Sparse Partitions

Arnold Filtser (Bar-Ilan university)

Wednesday, 24.07.2024, 12:45

Amado 814

A partition mathcal{P} of a metric space (X,d_X) is (sigma,tau,Delta)-sparse if each cluster has a diameter at most Delta, and every ball of radius Delta/sigma intersects at most tau clusters. In this talk, we will explore the construction and different applications of sparse partitions in their various forms over the years. As time allows, we will discuss applications to: Universal TSP, Steiner point removal, universal Steiner tree, and facility location....

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Prediction of Model Editing Success

Yanay Soker

Wednesday, 24.07.2024, 11:00

In LLMs, the ability to update factual knowledge of the model is an important and attractive ability, due to the need to deal with everchanging nature of information in the world. However, predicting whether an edit applied to a LLM will be successful or not is difficult. In this work, we suggest two metrics that can predict the editing success: (1) where the knowledge is stored in the parameters as reflected by the logit-lens technique; (2) the proba...

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"Research on the bar" night

Monday, 22.07.2024, 19:00

Taub Terrace

Please register here:
https://docs.google.com/forms/d/e/1FAIpQLSe8pYE1eVqgTK1xTf-4xyCEca4-bx12RuXtkXMcC5-4-ue_sQ/viewform ...

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Distance problems for typical norms

Noga Alon (Princeton and Tel Aviv University)

Monday, 22.07.2024, 15:30

Amado 232

"Noga Alon is being awarded the 2024 Wolf Prize for his profound impact on Discrete Mathematics and related areas."
I will describe a recent joint work with Matia Bucic and Lisa Sauermann about the investigation of extremal problems in discrete geometry for typical norms.
The results include surprisingly tight solutions of the unit and distinct distances problems for typical norms, as well as a determination of the chromatic number of the unit distance grap...

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Google are calling to all Code Wizards

Wednesday, 17.07.2024, 13:00

Taub 2

Calling all Code Wizards!
Ever wondered what it's like to be a software engineer at the heart of Google's innovation? Well, grab your coding wands and get ready to find out!Get a behind-the-scenes look on how Google connects 2 billion daily users through the OneGoogle team....

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Improved Approximations for Vector Bin Packing via Iterative Randomized Rounding

Ariel Kulik

Wednesday, 17.07.2024, 13:00

Amado 814

The talk will focus on the d-dimensional vector bin packing problem. The input for the problem is a set of items, each associated with a d-dimensional weight vector. The objective is to partition the items into a minimal number of bins, such that the total weight of items in a bin is at most one in every dimension. The problem...

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On the Algorithmic Foundations of Task Assistance Planning

Eitan Bloch

Wednesday, 17.07.2024, 09:30

Zoom Lecture: 98269146871 & Computational Robotics Lab (CRL), 1st floor, Taub building

In this work we introduce the problem of task assistance planning where we are given two robots Rtask and Rassist. The first robot, Rtask, is in charge of performing a given task by executing a precomputed path. The second robot, Rassist, is in charge of assisting the task performed by Rtask using on-board sensors. The ability of Rassist to provide assistance to Rtask depends on the locations of both robots. Since Rtask is moving along its path, Rassist may also need to move to...

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Hardware Sub-circuit Recognition via Subgraph Localization

Hardware Reverse Engineering (HRE) involves gate-level Netlist extraction and specification discovery, wherein graph-based methods play a crucial role in identifying sub-circuits. We propose a novel approach for Subcircuit Recognition, essential for specification discovery in HRE, leveraging existing graph similarity kernels. Focusing on the reduced problem we formulate as Subgraph Localization, we delineate two key components: graph similarity metr...

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Discovering and Erasing Unsafe Concepts

Niv Cohen

Tuesday, 16.07.2024, 11:30

Room 1061, Meyer Building

The rapid growth of generative models allows an ever-increasing variety of capabilities. Yet, these models may also produce undesired content suc...

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Interactions between Blockchain Networks Based on Market Analysis

Cryptocurrencies have gained popularity in recent years. They are often implemented on one or more of the over 1000 existing blockchain networks, including the popular Bitcoin and Ethereum networks. Recently, various technologies known as blockchain interoperability have been developed to connect these different blockchains, creating an interconnected blockchain ecosystem. Interoperability refers to the ability of blockchains to share information with each other. Decentralized Exc...

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Theory Seminar: Learning Probability Distributions; What Can, What Can't Be Done

Shai Ben-David (University of Waterloo)

Wednesday, 10.07.2024, 13:00

Amado 814

Characterizing learnability by a combinatorial dimension is a hallmark of machine learning theory. Starting from the fundamental characterization of binary classification PAC by the VC-dimension, through the characterization of online mistake bound learnability by the Littlestone dimension, all the way to recent papers characterizing multi-class learning and differential privacy.
However, for some basic learning setup no such characterizations have been provided.
...

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Large Scale Studies Of Deep Neural Networks For Uncertainty Estimation And Class-out-of-Distribution Detection

In this seminar, I will discuss two of my papers (published in ICLR 2023) on uncertainty estimation and class-out-of-distribution detection. We present a novel framework to benchmark the ability of image classifiers to detect class-out-of-distribution (C-OOD) instances (i.e., instances whose true labels do not appear in the training distribution) at various levels of detection difficulty. We apply this technique to ImageNet, and benchmark 500+ pretrained, publicly available, Image...

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Spotlight Day - Nvidia

Wednesday, 10.07.2024, 12:30

CS Taub Lobby

First spotlight day of the semester!!
Nvidia is coming to meet you in Taub
Wednesday, July 10, between 12:30-2:30 PM in the Taub lobby
In the program: a meeting with the recruitment team, the engineers
waiting for you!!...

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Designing Language Models to Think Like Humans

Chen Shani (Stanford)

Wednesday, 10.07.2024, 12:30

Taub 337

While language models (LMs) show impressive text manipulation capabilities, they also lack commonsense and reasoning abilities and are known to be brittle. In this talk, I will suggest a different LMs design paradigm, inspired by how humans understand it. I will present two papers, both shedding light on human-inspired NLP architectures aimed at delving deeper into the meaning beyond words.
The first paper [1] accounts for the lack of commonsense and reasoning abilities by pr...

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Pixel Club - On The Benefits Of Models With Perceptually Aligned Gradients

Roy Ganz - The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering

Tuesday, 09.07.2024, 11:30

Zoom Lecture: 94860001040

Deep learning has revolutionized computer vision, achieving unprecedented performanc in tasks like classification and detection. However, these models are highly susceptible to adversarial attacks, prompting the development of robust training methods. A notable outcome of such training is the phenomenon of Perceptually Aligned Gradients (PAG), where input gradients align semantically with human perception. Our research explores both the practical and theoretical implications of...

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Sequential Signal Mixing Aggregation For Message Passing Graph Neural Networks

Almog David

Wednesday, 03.07.2024, 11:00

Room 014

Message Passing Graph Neural Networks (MPGNNs) have emerged as the standard method for modeling complex interactions across diverse graph entities. While the theory of such models is widely investigated, their aggregation module has not received sufficient attention. Sum-based aggregators have solid theoretical foundations regarding their separation capabilities. However, practitioners often prefer using more complex aggregations and mixtures of diverse aggregations.
In this resea...

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Technipwn Group flies you to Vegas!

Tuesday, 02.07.2024, 17:00

Taub 9

Technipwn Group flies you to Vegas!This August, the faculty CTF group is participating in the DEFCON conference in Las Vegas and invites you to join!This coming tuesday we will hold a competition aimed at choosing the team that will fly to represent us at the conference.The topics of the competition are the usual topics of the ctf competitions such as: web, reverse, crypto, pwn and more!You are invited to prepare and come to compete ...

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Wrong Mathematical Proofs for Possibly Correct Claims: Implications and Applications in Cyber Security and Chip Design

Prof. Oded Margalit (Ben Gurion University)

Tuesday, 02.07.2024, 14:30

Taub Auditorium 012

In this talk, we'll explore intriguing parallels between incorrect mathematical proofs and critical failures in technology, demonstrating their impact and lessons learned, like:
1. Mathematical Proofs and Social Engineering: How hand-waving a mathematical proof is similar to social engineering, and why larger hardware implementations might surprisingly be more efficient.
2. Cybersecurity and Mathematical Proofs: The surprising connection between a cyber attack that nearly caused a...

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What Can be Learned from Mixing Memory Pages of Different Sizes

Cycle-accurate simulations, frequently used by computer architects, incur substantial overheads. To mitigate this, recent virtual memory studies have adopted a lighter-weight methodology that leverages partial simulations of only the memory subsystem. This approach feeds simulation outputs into a mathematical linear model to predict execution runtimes. While this methodology accelerates the simulation process, its accuracy has traditionally been assumed rather than rigorously v...

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Come be Part of the Faculty's CTF Group - Meeting on June 30th

Sunday, 30.06.2024, 18:30

Taub 9

Come be part of the Faculty's Capture The Flag - CTF group!!
The faculty CTF group, Technipwn, holds bi-weekly meetings where we solve challenges, train for competitions and have fun
The meetings are suitable for both beginners and experienced participants, and are held on Sundays at Taub 9.
Organized and managed by students, and intended for all students (not only from computer science!), with and without CTF experience. The sessions include practical experience in solving chal...

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GradHC: Highly reliable Clustering Algorithm for DNA Storage Systems

As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original i...

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ceClub: Advancing Computer Science with Digital Processing in Memory

The memory wall bottleneck is throttling the performance of data-intensive applications as the data transfer between the processing units (e.g., CPU, GPU cores) and the memory is significantly slower than the compute itself. Therefore, emerging digital processing-in-memory systems overcome this bottleneck by performing parallel bitwise logic within the memory arrays themselves. This enables a drastic reduction in data transfer for vectored operations since the same instruction may...

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ceClub: Enhancing Computer Performance with memristive Memory Processing Units: From General-Purpose Automation to DNA Sequencing Acceleration

Computer systems that facilitate tight integration of data storage and processing can eliminate the "memory wall" and "power wall" bottlenecks. The memristive Memory Processing Unit (mMPU) architecture contains memory cells that can also execute logical functions, such as Memristor-Aided Logic (MAGIC) NOR gates. The mMPU supports both general-purpose computing and application-specific acceleration. This research contributes to both these aspects of the mMPU.First, we intro...

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Advancing Molecular Frontiers: Redefining Optimization Goals in Drug Design

Deep learning has revolutionized drug development by enhancing various stages of the process, yet traditional optimization goals often lack the sophistication required for real-world applications. This work tackles more complex and nuanced problems beyond conventional property enhancement, focusing on optimizing under patentability constraints and translating preclinical success in animals to human clinical trials. In addressing patentability, we introduce a patent loss mechanism ...

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Theory Seminar: Fast Approximate Counting of Cycles

Tomer Even (Technion)

Wednesday, 26.06.2024, 13:00

Amado 719

We consider the problem of approximate counting of triangles and longer fixed length cycles in undirected and directed graphs. We provide an algorithm which is faster as t, the number of copies of the searched subgraph, increases. Our running time, which significantly improves upon the state of the art (Tětek [ICALP’22]), is the same as that of multiplying an n x (n/t) matrix by an (n/t) x n matrix, up to polylogarithmic factors. Finally, we show that under popular fine-graine...

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CE Club: Persistent Memory Programming without Persistent Memory

The arrival of persistent memory devices to consumer market has revived the interest in transactional durable algorithms. Persistent memory is touted as having two attributes that distinguish it from other storage technologies: fine access granularity and fast persistece.In the first part of the talk we investigate how these attributes differentiate persistent memory from block storage in the context of buffered durability – a relaxed approach that allows some progres...

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Pixel Club: Robust and Fast Volumetric Tomography of Natural Objects for Climate Studies

Computed tomography (CT) aims to recover the volumetric three dimensional (3D) structure of heterogeneous objects. Traditionally, CT refers to a medical imaging modality. There the object, radiation source and detector array are fully controlled. This results in a linear image formation model. In contrast, we seek CT of natural objects, acquired outdoors, in an uncontrolled environment. We focus on underwater plankton and cloud droplets, which strongly affect climate. Cloud imagin...

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Multivariate Time Series Prediction

Multivariate time series forecasting is a pivotal task in several domains, including financial planning, medical diagnostics, and climate science. This paper presents the Neural Fourier Transform (NFT) algorithm, which combines multi-dimensional Fourier transforms with Temporal Convolutional Network layers to improve both the accuracy and interpretability of forecasts. The Neural Fourier Transform is empirically validated on fourteen diverse datasets, showing superior performance ...

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The Annual Faculty Hackathon is Underway! CS Hackathon - Doing Good

Thursday, 20.06.2024, 08:00

Taub Building

The annual Faculty Hackathon is underway! CS Hackathon - Doing Good
Reserve the dates: June 20-21, in Taub.
This year, in accordance with the order of the day, we will focus on developing technological solutions to increase mental resilience in cooperation with the Ministry of Defense's rehabilitation department, associations and initiatives.
Opening of registration, development ideas, and preparation workshops - very soon.
For details and more informat...

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Theory Seminar: Approximate Matrix Multiplication via Spherical Convolutions

Omri Weinstein (Hebrew University)

Wednesday, 19.06.2024, 13:00

Amado 619

We develop a new framework, Polyform, for fast approximate matrix multiplication (AMM), through sums of sparse polynomial multiplications, using (variants of) FFT. Using this framework, we obtain new data-dependent speed-accuracy tradeoffs, which often improve on the worst-case accuracy of randomized sketching algorithms. Meanwhile, Polyform can be viewed as a cheap alternative bilinear operator to matrix multiplication in Deep Neural Networks, which is our motivating applicati...

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Quantitative Semantics on Jumping Finite Automata

Jumping automata are finite automata that read their input in a non-sequential manner, by allowing a reading head to “jump” between positions on the input, consuming a permutation of the input word. We argue that allowing the head to jump should incur some cost. To this end, we propose three quantitative semantics for jumping automata, whereby the jumps of the head in an accepting run define the cost of the run. The three semantics correspond to different interpretations of ju...

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Curvature Shift in Neural Networks

Neural network optimization is a challenging task, in large part because of the nonconvexity, in general, of the loss function. Various algorithms have been proposed to solve this task, but most of them focus on the convex subspaces of the loss function to avoid the challenge of finding the optimal step in concave subspaces. In addition to avoiding the concave spaces entirely (which can bear a significant computational burden!), they also simply take the minimizer of a second-d...

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Protected Test-Time Domain Adaptation via Online Entropy Matching

Yarin Bar

Sunday, 16.06.2024, 11:00

Taub 9

In this paper, we present a novel approach for test-time domain adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adap...

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Pixel-Club:Volumetric Signal Recovery and Calibration in Atmospheric Lidar

Adi Vainiger, The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering

Monday, 10.06.2024, 15:00

Room 1061, EE Meyer Building

Atmospheric lidars are important remote sensing tools in aerosols and climate research. A pulsed time-of-flight lidar continuously samples vertical atmospheric profiles, through day and night, yielding a spatiotemporal atmospheric map. However, lidar analysis is challenged by low signal-to-noise ratios, sunlight interference, and need for frequent calibration. We advance lidar analysis. We develop a framework for simulating realistic spatiotemporal lidar data under diverse atmosph...

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Covering All Bases: The Next Inning in DNA Sequencing Efficiency

DNA emerges as a promising medium for the exponential growth of digital data due to its density and durability. This study extends recent research by addressing the coverage depth problem in practical scenarios, exploring optimal error-correcting code pairings with DNA storage systems to minimize coverage depth. Conducted within random access settings, the study provides theoretical analyses and experimental simulations to examine the expectation and probability distribution of sa...

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Theory Seminar: Explicit Codes for Poly-Size Circuits and Functions that are Hard to Sample on Low Entropy Distributions

Ronen Shaltiel (Haifa University)

Wednesday, 05.06.2024, 12:45

Amado 719

In the talk I will survey a research direction which combines coding-theory and computational complexity. This direction was introduced by Lipton, and refined by Guruswami and Smith. The goal is to construct error correcting codes which correct errors that are introduced by computationally bounded channels. This is in contrast to Hamming’s standard model which assumes a bound on the fraction of bits that a channel may flip, but allows the channel to be computationally unbounded....

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CE-Club: Safe Deployment of Deep Learning Solutions for Rate Control Problems

In recent years, machine learning (ML) has fueled extraordinary advances in fields such as natural language processing and computer vision. These results have encouraged researchers to utilize ML in additional application domains, such as computer and networked systems, with varying degrees of success. In this talk I will focus on the prominent challenge of rate control in computer networking, and devising a practical ML toolkit for the safe deployment of deep-learning-based solut...

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The CTF Group Of The Faculty - Technipwn - Invites You To The Opening Lecture Of The Semester 4.6.2024

Tuesday, 04.06.2024, 18:30

Taub 1

The CTF group of the faculty - Technipwn invites you to the semester opening lecture - a year of offensive security: attacks on OpenAI, Atlassian, Apple and more!
Speaker>> Ron Massas, Vulnerability Researcher at Imperva
During his work at Imperva, security researcher Ron Massas uncovered a number of fascinating vulnerabilities and hacks in a number of large companies.
In the lecture, he will take us on a fascinating journey in the world of offensive security, and will present c...

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Efficient, Correct and Durable Concurrent Algorithms

In the last two decades, a main way to improve performance of computer processors has been producing processors with multiple cores, on which tasks may execute concurrently. Software is required to keep up with the hardware advances and supply concurrent algorithms for programs that run on multiple cores. This talk will focus on a fundamental building block of concurrent algorithms: concurrent data structures, designed with improved efficiency while also satisfying strong correctn...

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Open day for undergraduate studies at the Technion May 31 in Sarona, Tel Aviv

Friday, 31.05.2024, 10:00

Sarona, Tel Aviv

An open day for undergraduate studies at the Technion will be held on May 31 from 10:00 am to 1:00 pm in Sarona, Tel Aviv.
At the event you can meet the representatives of the faculty, consult, get an impression and get answers about the studies.
Register for the open day at the ...

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Theory Seminar

Theory group members and interested people

Wednesday, 29.05.2024, 12:45

Amado 619

This week we plan to get acclimated to our seminar's new location (outside of Taub, due to construction work), in a more social atmosphere. This includes introductions of current and prospective members of the group,* and talk about plans for the semester, including theory courses scheduled. Come meet established theorists and folks interested in theory research!
* if you know students doing or interested in theory who are not (or might not be) registered to this mailing list, pl...

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Robustness Verification of Multi-Label Neural Network Classifiers

Multi-label neural networks are important in various tasks, including safety-critical tasks. Several works show that these networks are susceptible to adversarial attacks, which can remove a target label from the predicted label list or add a target label to this list. However, no verifier can deterministically determine the list of labels for which a multi-label neural network is locally robust. The main challenge is that the complexity of the analysis increases by a factor expon...

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CE-Club: Robustness Verification of Multi-Label Neural Network Classifiers

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The Sky Is The Limit - An Open Day For Advanced Degree Studies At The Faculty

Wednesday, 22.05.2024, 13:00

Taub 337

A meeting of those interested in graduate studies at the Taub Faculty of Computer Science at the Technion will be held on Wednesday, May 22, 2024, 1:00 p.m., room 337
Outstanding bachelor's graduates from all universities!
This is your opportunity to participate and be impressed by the Faculty of Computer Science at the Technion, Meet faculty members and graduate students and hear fascinating lectures.
Event schedule:
13:00-13:30 Opening remarks:
The words of t...

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CE-Club: Survivable Payment Channel Networks

Yekaterina Podiatchev

Wednesday, 22.05.2024, 11:30

Meyer 861

Payment Channel Networks (PCNs) are a leading method to scale the transaction throughput in cryptocurrencies. Two participants can use a bidirectional payment channel for making multiple mutual payments without committing them to the blockchain. Opening a payment channel is a slow operation that involves an on-chain transaction locking a certain amount of funds. These aspects limit the number of channels that can be opened or maintained. Users may route payments through a multi-ho...

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Structural Language Models for Code

In the past few years, software has been at the heart of many applications ranging from appliances to virtual services. Helping software developers to write better code is a crucial task. In parallel, recent developments in machine learning and deep learning in particular have shown great promise in many fields, and code-related tasks in particular. The main challenge is how to represent code in a way that can be used by deep learning models effectively. While code can be treated ...

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Jumping Automata over Infinite Words

We introduce and study jumping automata over infinite words, a fascinating twist on traditional finite automata. These machines read their input in a non-consecutive manner, defying conventional word order. We explore three distinct semantics: one ensuring every letter is accounted for, another permitting word permutation within fixed windows, and a third allowing permutation within windows of an existentially-quantified bound. Our work covers expressiveness, closure properties, a...

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CIQA : A Coding Inspired Question Answering Model

Methods in question-answering (QA) that transform texts detailing processes into an intermediate code representation, subsequently executed to generate a response to the presented question, have demonstrated promising results in analyzing scientific texts that describe intricate processes.
The limitations of these existing text-to-code models are evident when attempting to solve QA problems that require knowledge beyond what is presented in the input text. We propose a novel domai...

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Blockchain Loans: Mechanisms, Platforms And Challenges

Distributed lending protocols are major financial mechanisms that match borrowers and lenders without the need for intermediaries. Smart contracts deployed on blockchains ensure the security of such loans. Borrowers mitigate the risk of default by providing collateral in the form of cryptocurrencies or by limiting the loan duration.
In the first part of the talk, we describe various loan types, major platforms, and financial models that allow them. In the second part of the talk,...

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Pixel Club: Deep Computational Imaging: Optimal sensing, reconstruction, and uncertainty quantification

Elias Nehme - The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering

Tuesday, 16.04.2024, 14:30

Room 1061, EE Meyer Building & Zoom Lecture:5049603009

In biological imaging, fast acquisition of depth information is crucial e.g. for accurate 3D tracking of sub-cellular elements and for 3D super-resolution. In the first part of this talk, we present a series of works enhancing the success of snapshot depth sensing in the revolutionary field of single-molecule localization microscopy (Nobel Prize in Chemistry 2014). Specifically, we present an approach for jointly designing the “optics” of the microscope and the 3D reconstructi...

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Deep Learning and Tree Search-Based Policy Optimization under Stochastic Execution Delay

The standard formulation of Markov decision processes (MDPs) assumes that the agent's decisions are executed immediately. However, in numerous realistic applications such as robotics or healthcare, actions are performed with a delay whose value can even be stochastic. In this work, we introduce stochastic delayed execution MDPs, a new formalism addressing random delays without resorting to state augmentation. We show that given observed delay values, it is sufficient to perform a ...

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A VIP Visit At The Technion For Children Of Graduates

Monday, 15.04.2024, 10:00

Technion, Ulman Building

Graduates of the faculty are invited with their children to a day of lectures, workshops and information regarding registration and admission - for children; and a nostalgic tour (for parents)
Monday, April 15, starting at 10:00 at the Technion
In the program:
1. The wonders of infinity - the Faculty of Mathematics
2. What is the Internet of Things? - Faculty of Computer Science
3. Games and Virtual Reality - Fac...

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CE-Club: Buffer Isolation With Imperfect Congestion Control Classification

Due to their increasing aggressiveness, recent congestion control algorithms (CCAs) can quickly starve standard TCP flows in their shared router queues. Existing solutions based on fair queueing are not scalable enough, and those based on admission control do not fit all CCAs. Independently, building on the popularization of machine learning, recent papers have designed several CCA classifiers.In this seminar, we introduce a buffer isolation mode where incoming flows first...

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Pixel Club: FreeAugment: Data Augmentation Search Across All Degrees of Freedom

Tom Bekor - The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering

Tuesday, 09.04.2024, 11:30

Room 1061, EE Meyer Building

Graduate Seminar
Data augmentation has become an integral part of deep learning, as it is known to improve the generalization capabilities of neural networks.Since the most effective set of image transformations differs between tasks and domains, automatic data augmentation search aims to alleviate the extreme burden of manually finding the optimal image transformations. However, current methods are not able to jointly optimize all degrees of freedom: (1)...

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PAT-CEP : Statistical Guarantee for ML-based Complex Event Processing

In the landscape of data stream processing, the challenges posed by online problems are of paramount importance. This paper introduces a pioneering method tailored to address these challenges within the context of data streams. While our initial focus centered on Complex Event Processing (CEP) problems, it is essential to underscore the versatility of our approach, as it is equally applicable to a diverse range of online problems. To the best of our knowledge, there exists no comp...

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TDC Seminar: Fault-Tolerant Labeling and Compact Routing Schemes

Michal Dory, Haifa University

Monday, 08.04.2024, 13:30

Zisapel (ECE) 608

Assume that you have a huge graph, where edges and vertices may fail, and you want to answer questions about this graph quickly, without inspecting the whole graph. A fault-tolerant labeling scheme allows us to do just that. It is a distributed data structure, in which each vertex and edge is assigned a short label, such that given the labels of a pair of vertices s,t, and a set of failures F, you can answer questions about s and t in G\F. For example, determine if s and t are con...

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DEPTH: Discourse Education through Pre-Training Heirarchically

Zachary Elisha Bamberger

Sunday, 07.04.2024, 10:00

Room 601

Language Models (LMs) excel in many tasks, but understanding discourse – how sentences connect to form coherent text – remains a challenge. This is especially true for smaller models aiming to match the abilities of their larger counterparts in handling long and complex inputs. To address this, we introduce DEPTH, a new encoder-decoder model designed to foster robust discourse-level representations during the pre-training phase. DEPTH uniquely combines hierarchical sentence re...

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Learning with visual foundation models for Gen AI

Gal Chechik, Bar-Ilan University and NVIDIA

Thursday, 04.04.2024, 10:30

Taub 337

Between training and inference, lies a growing class of AI problems that involve fast optimization of a pre-trained model for a specific inference task. These are not pure “feed-forward” inference problems applied to a pre-trained model, because they involve some non-trivial inference-time optimization beyond what the model was trained for; neither are they training problems, because they focus on a specific input. These compute-heavy inference workflows raise new challenges i...

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CS RESEARCH DAY 2024

Wednesday, 03.04.2024, 12:30

CS Taub Lobby

The 12th CS Research Day for graduate studies will be held on Wednesday, April 03, 2024 between 12:30-14:30, at the lobby of the CS Taub Building.
Research Day events are opportunity for our graduate students to expose their researches using posters and presentations to CS faculty and all degrees students, Technion distinguished representatives and to high-ranking delegates from the hi-tech leading industry companies in Israel and abroad.
The participating researches will be on...

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Theory Seminar: Toward Better Depth Lower Bounds: A KRW-like theorem for Strong Composition

Or Meir (Haifa University)

Wednesday, 03.04.2024, 12:15

Taub 201

One of the major open problems in complexity theory is proving super-logarithmic lower bounds on the depth of circuits. Karchmer, Raz, and Wigderson (Computational Complexity 5(3/4), 1995) suggested approaching this problem by proving that depth complexity of a composition of two functions is roughly the sum of their individual depth complexities. They showed that the validity of this conjecture would imply the desired lower bounds.The intuition that underlies the KRW conj...

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CE-Club:Here Comes the GenAI Malware, Worm, and APT

Ben Nassi, Technion

Wednesday, 03.04.2024, 11:30

Meyer 861

In the past year, numerous companies have incorporated Generative AI (GenAI) capabilities into new and existing applications, forming interconnected Generative AI (GenAI) ecosystems consisting of applications powered by GenAI services.While ongoing research highlighted risks associated with the GenAI layer of agents (e.g., dialog poisoning, membership inference, prompt leaking, jailbreaking), a critical question emerges: Can attackers develop malware to exploit the GenAI c...

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Marrying Vision and Language: A Mutually Beneficial Relationship?

Hadar Averbuch-Elor, Tel-Aviv University

Tuesday, 02.04.2024, 14:30

Taub 337

Foundation models that connect vision and language have recently shown great promise for a wide array of tasks such as text-to-image generation. Significant attention has been devoted towards utilizing the visual representations learned from these powerful vision and language models. In this talk, I will present an ongoing line of research that focuses on the other direction, aiming at understanding what knowledge language models acquire through exposure to images during pretraini...

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Pixel-Club: STMPL: Human Soft-Tissue Simulation

Anton Agafonov (Graduate Seminar)

Tuesday, 02.04.2024, 11:30

Room 608, Zisapel Building

In various applications, such as virtual reality and gaming, simulating the deformation of soft tissues in the human body during interactions with external objects is essential. Traditionally, Finite Element Methods (FEM) have been employed for this purpose, but they tend to be slow and resource-intensive. In this paper, we propose a unified representation of human body shape and soft tissue with a data-driven simulator of non-rigid deformations. This approach enables rapid simula...

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StarkWare - Engineering the Future of Blockchain

StarkWare

Monday, 01.04.2024, 12:30

The graduate students' lounge, room 225

Engineering the Future of Blockchain
1.4.23 | 12:30-14:30 | The graduate students' lounge, Taub, 2nd floor
12:30 - Mingling and Snacks
13:00 - Oded Naor, Ph.D., Product Manager - Btockchain reinvented: a deep dive into StarkWare's game-changing sotutions
13:30 - Noa Oved, MSC, Software Team Lead - Coding the future: Transforming algorithmic insights into real-wortd sotutions
14:00- Q&A
Plese RSVP at...

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Open day for March 28. Getting Started!

Thursday, 28.03.2024, 09:30

Technion

An open day for studies at the Technion will be held on Thursday, March 28, starting at 09:30
For details and registration, go to the registration and admission website at the link
...

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Theory Seminar: Exploring the Generalization Ability of (Convex) Optimization Algorithms

Tomer Koren (Tel-Aviv University)

Wednesday, 27.03.2024, 12:15

Taub 201

In machine learning, there has been considerable interest over the past decade in understanding the ability of optimization algorithms to generalize—namely, to produce solutions (models) that extend well to unseen data—particularly in the context of overparameterized problems. I will survey several recent theoretical studies that explore generalization in classical convex optimization, that reveal intriguing behavior of common optimization methods and shed some light on the c...

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Guest seminar - How to design innovative software?

Prof. Daniel Jackson, MIT

Wednesday, 27.03.2024, 12:00

Auditorium 012, floor 0

I will explain how successful innovations in software can usually be traced to just one or two "concepts" that offer new scenarios that, with seemingly small shifts, radically change how an application is used. I give examples from apps such as Zoom, WhatsApp and Calendly. I explain how concepts, each with their own characteristic scenarios, can be composed to form apps. I explain how this idea can be used to make software more usable, modular and consistent.
More information at:...

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CE-Club: Optimizing Information Freshness in Wireless Networks: From Theory to Implementation

Eytan Modiano - Laboratory for Information and Decision Systems Massachusetts Institute of Technology

Wednesday, 27.03.2024, 11:30

Meyer 861

Age of Information (AoI) is a recently proposed performance metric that captures the freshness of the information from the perspective of the application. AoI measures the time that elapsed from the moment that the most recently received packet was generated to the present time. In this talk, we explore the AoI optimization problem in wireless networks.We start by considering a wireless network with a number of nodes transmitting information to a base station and develop l...

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Intel's tech experience is coming to campus!

Wednesday, 27.03.2024, 10:00

EE Meyer Building

Intel's tech experience is coming to campus!
Ready for the most innovative technological picnic you've ever seen?
We have loaded technological tools and our greatest minds to the track and we are on our way to you
Wednesday 27.3 | Mayer building, 3rd floor
10:00 - AR | VR | HR Come and get to know us and our technologies
12:30 - FPGA MicroPython
The number of places is limited, register at the ...

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Pixel-Club: Idempotent Generative Network

Assaf Shocher, NVIDIA

Tuesday, 26.03.2024, 11:30

Room 1061, EE Meyer Building

We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely f(f(z))=f(z). The proposed model f is trained to map a source distribution (e.g, Gaussian noise) to a target distribution (e.g. realistic images) using the following objectives: (1) Instances from the target distribution should map to themselves, namel...

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The Altru-Egoistic Approach to Collaborative Caching

In this lecture we will explore collaborative caching algorithms in order to boost the effectiveness of caches in a distributed storage system. I'll introduce a scheme that partitions each node’s cache into two conceptual regions: an egoistic area whose goal is to contain the most valuable data for the node that owns the cache, and an altruistic area whose goal is to contain the most valuable data for the system as a whole. Each node’s division between these two regions is dyn...

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Pixel-Club: Explaining Classification by Image Decomposition

Elnatan Kadar (Graduate Seminar)

Wednesday, 20.03.2024, 14:30

Room 1061, EE Meyer Building

We propose a new way to explain and to visualize neural network classification through a decomposition-based explainable AI.
Instead of providing an explanation heatmap, our method yields a decomposition of the image into class-agnostic and class-distinct parts, with respect to the data and chosen classifier. Following a fundamental signal processing paradigm of analysis and synthesis, the original image is the sum of the decomposed parts. We thus obtain a radically different way ...

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Theory Seminar: Deterministic Online embedding of metrics

Ilan Newman (University of Haifa)

Wednesday, 20.03.2024, 13:15

Taub 201

A finite metric space $(X,d)$ on a set of points $X$ is just the shortest path metric $d$ on a positively weighted graph $G=(X,E)$. In the online setting, the vertices of the input finite metric space $(X,d)$ are exposed one by one, together with their distances $d(*,*)$to the previously exposed vertices. The goal is to embed (map) $X$ into a given host metric space $(H,d_H)$ (finite or not) and so to distort the distances as little as possible (distortion is the worst cas...

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Mobileye Spotlight Day

Wednesday, 20.03.2024, 12:30

Taub Building

You are invited to the spotlight day of the Mobileye company at the Technion
Wednesday 20.3 | 12:30 | Taub Building...

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ceClub: From Dashboards To Labels: Helping Users Manage And Make Decision About Privacy

(George Washington University) Adam J Aviv

Wednesday, 20.03.2024, 11:30

Meyer 861 & Zoom Lecture:94673013539

The surveillance economy, where tracking and collecting data on uses for the purpose of advertising and other actions, is central to much of the money-making enterprises of the modern technology ecosystem. Due to regulations and other forces, some of the largest companies, such as Google and Apple, have prioritized mechanisms for users to better manage and receive information about the kinds of data that is being collected about them. In this talk, I will explore how effective the...

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Yahoo Is Coming To The Faculty On March 19 - Summer Internships And Research Opportunities

Tuesday, 19.03.2024, 12:30

CS Grads Club, Room 225

Yahoo holds a dedicated meeting with graduate students
Tuesday, March 19 at 12:30 p.m. at the CS Grads Club
In the program: an introduction to Yahoo's research in Israel and the summer internship program.
Register to the event here
waiting for you!!...

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Nvidia - Lecture And Pizza

Yarden Zuckerman, SW Security Manager, Nvidia

Monday, 18.03.2024, 18:00

Auditorium 012, floor 0

Join us for a lecture and pizza!
Cyber Security Challenges In The Modern Era
by Yarden Zuckerman, SW Security Manager, Nvidia
Monday | March 18, 2024 | 18:00 p.m. | Piano Auditorium Taub Building
...

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TDC Seminar: Null Messages, Information and Coordination

Raissa Nataf

Monday, 18.03.2024, 13:30

Zisapel (ECE) 608

I'll present our paper about the role that null messages play in synchronous systems with and without failures. Our work provides necessary and sufficient conditions on the structure of protocols for information transfer and coordination there. We start by introducing a new and more refined definition of null messages. A generalization of message chains that allow these null messages is provided and is shown to be necessary and sufficient for information transfer in reliable syste...

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Theory Seminar: A Unified Characterization Of Private Learning

Hilla Schefler (Technion)

Wednesday, 13.03.2024, 12:15

Taub 201

Differential Privacy (DP) is a mathematical framewirk for ensuring the privacy of individuals in a dataset. Roughly speaking, it guarantees that privacy is protected in data analysis by ensuring that the output of an analysis does not reveal sensitive information about any specific individual, regardless of whether their data is included in the dataset or not.This talk presents a unified framework for characterizing both pure and approximate differentially private learnabi...

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Window-Based Distribution Shift Detection for Deep Neural Networks

To deploy and operate deep neural models in production, the quality of their predictions, which might be contaminated benignly or manipulated maliciously by input distributional deviations, must be monitored and assessed. Specifically, we study the case of monitoring the healthy operation of a deep neural network (DNN) receiving a stream of data, with the aim of detecting input distributional deviations over which the quality of the network’s predictions is potentially damaged. ...

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Active Propulsion Noise Shaping For Multi-Rotor Aircraft Localization

Multi-rotor aerial autonomous vehicles (MAVs) primarily rely on vision for navigation purposes. However, visual localization and odometry techniques suffer from poor performance in low or direct sunlight, a limited field of view, and vulnerability to occlusions. Acoustic sensing can serve as a complementary or even alternative modality for vision in many situations, and it also has the added benefits of lower system cost and energy footprint, which is especially important for micr...

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Pixel-Club: Using Zodiacal Light For Spaceborne Calibration Of Polarimetric Imagers

Or Avitan (Graduate Seminar)

Tuesday, 12.03.2024, 11:30

Room 1061, EE Meyer Building

We propose that spaceborne polarimetric imagers can be calibrated, or self-calibrated using zodiacal light (ZL). ZL is created by a cloud of interplanetary dust particles. It has a significant degree of polarization in a wide field of view. From space, ZL is unaffected by terrestrial disturbances. ZL is insensitive to the camera location, so it is suited for simultaneous cross-calibration of satellite constellations. ZL changes on a scale of months, thus being a quasi-constant tar...

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TDC Seminar: From Distributed Computing to Cryptography and Back

Ittai Abraham, Intel

Monday, 11.03.2024, 13:30

Zisapel (ECE) 608

I will share some of my learnings from working on problems on the intersection of Distributed Computing and Cryptography.On the one hand, I will show how some cryptographic protocols (MPC and DKG) can be improved by using distributed computing counterparts for notions such as zero knowledge proofs and proofs of knowledge. On the other hand, I will show how distributed computing protocols (ABA) can be improved by carefully using notions of binding from cryptography. One rec...

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On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method

Most works on modeling the conversation history in Conversational Question Answering (CQA) report a single main result on a common CQA benchmark. While existing models show impressive results on CQA leaderboards, it remains unclear whether they are robust to shifts in setting (sometimes to more realistic ones), training data size (e.g. from large to small sets) and domain. In this work, we design and conduct the first large-scale robustness study of history modeling approaches for...

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Nvidia Invites You To a Virtual Spotlight Day 10/3

Sunday, 10.03.2024, 18:00

Meet virtually

Nvidia invites you to a virtual spotlight day where the company's engineers will talk about the different groups and open positions
Sunday, March 10, from 18:00 to 19:30
To register for the event here...

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Cadence Arrive For Recruitment Day At The Faculty

Wednesday, 06.03.2024, 12:30

CS Taub Lobby

Cadence company is coming to a spotlight day at the faculty
Wednesday 6/3 | 12:30-14:30 | Lobby Taub
In the program: a meeting with the recruitment teams, the engineers for a 1:1 conversation about employment opportunities and tips for writing a report.
And of course merch and sweets.
waiting for you!!...

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Faster Matrix Game Solvers Via Ball Oracle Acceleration

Yair Carmon (Tel-Aviv university)

Wednesday, 06.03.2024, 12:15

Taub 201

We design a new stochastic first-order algorithm for approximately solving matrix games as well as the more general problem of minimizing the maximum of smooth convex functions. Our central tool is ball oracle acceleration: a technique for minimizing any convex function with a small number of calls to a ball oracle that minimizes the same function restricted to a small ball around the query point. To design an efficient ball oracle for our problems of interest we leverage stochast...

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Early Time Classification with Accumulated Accuracy Gap Control

Early time classification algorithms aim to label a stream of features without processing the full input stream, while maintaining accuracy comparable to that achieved by applying the classifier to the entire input. In this paper, we introduce a statistical framework that can be applied to any sequential classifier, formulating a calibrated stopping rule. This data-driven rule attains finite-sample, distribution-free control of the accuracy gap between full and early-time classifi...

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Mor Filo, a graduate of the faculty and a developer at Amazon

Monday, 04.03.2024, 18:30

Auditorium 012, floor 0

You have been accepted as a student! What now?
The student community at the SHE S faculty invites you to a meeting on:
First time student job: about the opportunities, challenges and skills you acquire in your first job
Monday, 4/3 at 6:30 pm in the piano auditorium
Please register in advance: here
Speaker: Moore Philo, graduate of the faculty and developer at Amazon
The meeting is suitable for thos...

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Stable Tuple Embeddings for Dynamic Databases

We study the problem of computing an embedding of the tuples of a relational database
in a manner that is extensible to dynamic changes of the database. In this problem,
the embedding should be stable in the sense that it should not change on the existing
tuples due to the embedding of newly inserted tuples (as database applications might
already rely on existing embeddings); at the same time, the embedding of all tuples, old
and new, should retain high quality. This task is chall...

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Spotlight Day For Nvidia At The Technion

Wednesday, 28.02.2024, 12:30

CS Taub Lobby

Spotlight day for Nvidia at the Technion on February 28, 2024
Nvidia is coming to meet the students of the Faculty of Computer Science at the Technion!
Come and meet the company's engineers face to face
Wednesday February 28, 2024 | 12:30-14:30 | Taub lobby, floor 0...

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Distance Sensitivity Oracles

Sarel Cohen (Tel-Aviv Academic College)

Wednesday, 28.02.2024, 12:15

Taub 201

Theory Seminar: An f-edge fault-tolerant distance sensitivity oracle (f-DSO) is a data-structure that, when queried with two vertices (s, t) and a set F of at most f edges of a graph G with n vertices, returns an estimate tilde{d}(s,t,F) of the distance d(s,t,F) from s to t in G – F. The oracle has stretch alpha if the estimate satisfies d(s,t,F) le tilde{d}(s,t,F) le alpha cdot d(s,t,F) . In the last two decades, extensive research has focused on developing efficient f-DSOs. Th...

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TDC Seminar: Fast and Fair Lock-Free Locks

Naama Ben-David, Technion

Monday, 26.02.2024, 13:30

Zisapel (ECE) 608

Locks are frequently used in concurrent systems to simplify code and ensure safe access to contended parts of memory. However, they are also known to cause bottlenecks in concurrent code, leading practitioners and theoreticians to sometimes opt for more intricate lock-free implementations. In this talk, I’ll show that, despite the seeming contradiction, it is possible to design practically and theoretically efficient lock-free locks; I'll present a lock-free lock algorithm with ...

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Fundamental Problems in AI: Transferability, Compressibility and Generalization

In this talk, we delve into several fundamental questions in deep learning. We start by addressing the question, "What are good representations of data?" Recent studies have shown that the representations learned by a single classifier over multiple classes can be easily adapted to new classes with very few samples. We offer a compelling explanation for this behavior by drawing a relationship between transferability and an emergent property known as neural collapse. Later, we expl...

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Theory and Practice of DNA Storage

In the last decade, DNA-based storage systems emerged as a potential data archival solution due to their high data density and durability. This research delves into intrinsic error characteristics of DNA storage systems to devise robust coding strategies and innovative algorithms for enhanced reliability, efficiency, scalability, and cost-effectiveness. The research propels DNA storage feasibility while contributing to foundational theory.The work analyzes combinatorial st...

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Identifying Underlying Geometry To Analyze High-Dimensional Data: Images And Shape Spaces As A Case Study

High-dimensional data is increasingly available in diverse applications, ranging from images to shapes represented as point clouds. Such data raises novel questions and offers a unique opportunity to study them by developing new machine-learning tools. While the analysis of an individual sample may be challenging, leveraging the power of the data collection can be effective in tackling complex tasks. This talk delves into the challenges associated with studying such data, particul...

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Theory Seminar: IOPs with Inverse Polynomial Soundness Error

Gal Arnon (Weizmann institute)

Wednesday, 21.02.2024, 12:15

Taub 201

We show that every language in NP has an Interactive Oracle Proof (IOP) with inverse polynomial soundness error and small query complexity. This achieves parameters that surpass all previously known PCPs and IOPs. Specifically, we construct an IOP with perfect completeness, soundness error 1/n, round complexity O(loglog n), proof length poly(n) over an alphabet of size O(n), and query complexity O(loglog n). This is a step forward in the quest to establish the sliding-scale conjec...

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TDC Seminar: Deterministic Distributed Maximum Weight Independent Set Approximation in Sparse Graphs

Yuval Gil, Technion

Monday, 19.02.2024, 13:30

Zisapel (ECE) 608

This talk focuses on the distributed task of constructing an approximate \emph{maximum weight independent set (MWIS)}.
Specifically, we are interested in deterministic CONGEST algorithms whose approximation guarantees are expressed as a function of the graph's \emph{arboricity} $\alpha$.Generally speaking, efficient deterministic non-trivial approximation algorithms for MWIS were not known until the recent breakthrough of Faour et al.~[SODA 2023] that obtained an $O(\Delta...

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A Deep Learning Platform for Diagnosing ECG Tests

In clinical settings, a significant portion of ECG data is typically available in printed form, and the most convenient means of digitizing this information involves utilizing a mobile device. Despite notable progress in AI-based techniques for paper-based 12-lead ECG analysis, their adoption in clinical practice remains limited primarily due to challenges such as inadequate accuracy in clinical settings and a restricted ability to diagnose various cardiac conditions.
Our objectiv...

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Theory Seminar: Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension

Idan Mehalel (Technion)

Wednesday, 14.02.2024, 12:15

Taub 201

Suppose that n forecasting experts (or functions) are providing daily rain/no-rain predictions, and the best among them is mistaken in at most k many days. For how many days will a person allowed to observe the predictions, but has no weather-forecasting prior knowledge, mispredict?In this talk, we will discuss how such classical problems can be reduced to calculating the (average) depth of binary trees, by using newly introduced complexity measures (aka dimensions) of the...

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ceClub: Space-efficient FTL for Mobile Storage via Tiny Neural Nets

Ron Marcus

Wednesday, 14.02.2024, 11:30

Meyer 861

With the rapid increase of storage demands and working sets of modern mobile apps, maintaining high I/O performance in mobile SSDs under strict resource constraints is challenging. The Flash Translation Layer (FTL) must increase the capacity of the Logical-To-Physical (L2P) address translation cache to keep up with the new workloads, but it comes at the cost of scaling the on-die SRAM, resulting in higher chip area, power consumption, and costs.In this talk, I will present...

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Technion CTF Team

Tuesday, 13.02.2024, 18:30

Taub 2

Come be part of a new Capture The Flag-CTF group at the Faculty
The meetings are held every Tuesday at 18:30 at Taub 9 and include guest lectures and practical experience in solving challenges.
Everybody is invited! Beginners and experienced
For details: Technionctf.com
This week - February 13, 2024:
18:30 | Taub 2 | Omar Atias - security researcher, lecturer at BlackHat USA & DEFCON
The price of con...

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A 0-RTT-Aware QUIC Load Balancer

QUIC is an emerging transport protocol, offering multiple advantages over TCP. Yet, to fully unleash QUIC’s potential, a paradigm shift is needed in existing network infrastructure. We propose a novel 0-RTT-aware load balancing algorithm. 0-RTT is crucial for web performance, particularly on mobile networks. Our load balancing algorithm ensures 0-RTT while maintaining near-optimal load balancing performance.Through extensive simulations, using both synthetic and real-wor...

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Final Spotlight Day

Wednesday, 07.02.2024, 12:30

Auditorium 012, floor 0

You are invited to Final spotlight day
Wednesday 07.02.2024 | 12:30-14:30 | Visitor Center Auditorium 012, Floor 0
12:30 - Come meet engineers, researchers and the recruitment team at Final, and get to know the day-to-day life at Final.
13:15 - Meeting on options, probabilities and the world of algorithm trading | Noam Horowitz - researcher at Final
To register for the lecture click ...

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Theory Seminar: Algorithmic Cheap Talk

Konstantin Zabaranyi (Technion)

Wednesday, 07.02.2024, 12:15

Taub 201

Come be part of a new Capture The Flag-CTF group at the Faculty
The meetings are held every Tuesday at 18:30 at Taub 9 and include guest lectures and practical experience in solving challenges.
Everybody is invited! Beginners and experienced
For details: Technionctf.com
The week of February 6, 2024:
Beginners: Forensics & Networks | Taub 9
experienced: Challenges from 2023 LA CTF | Tau...

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Technion CTF Team

Tuesday, 06.02.2024, 18:30

Taub 9

Come be part of a new Capture The Flag-CTF group at the Faculty
The meetings are held every Tuesday at 18:30 at Taub 9 and include guest lectures and practical experience in solving challenges.
Everybody is invited! Beginners and experienced
For details: Technionctf.com
The week of February 6, 2024:
Beginners: Forensics & Networks | Taub 9
experienced: Challenges from 2023 LA CTF | Taub 8...

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Constrictor: Immutability as a Design Concept

Elad Kinsbruner

Tuesday, 06.02.2024, 12:30

Taub 601

Many object-oriented applications in algorithm design rely on objects never changing during their lifetime. This is often tackled by marking object references as read-only, e.g., using the const keyword in C++. In other languages like Python or Java where such a concept does not exist, programmers rely on best practices that are entirely unenforced. While reliance on best practices is obviously too permissive, const-checking is too restrictive: it is possible for a method to mutat...

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Pixel-Club: Robustified ANNs Reveal Wormholes Between Human Category Percepts

Guy Gaziv (DiCarlo Lab at MIT)

Tuesday, 06.02.2024, 11:30

Room 1061, EE Meyer Building

The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations. Because human category reports (aka human percepts) are thought to be insensitive to those same small-norm perturbations — and locally stable in general — this argues that ANNs are incomplete scientific models of human visual perception. Consistent with this, we show that when small-norm image perturbations are generated by standard ANN mo...

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Theory Of Crypto Seminar: Perfect Asynchronous MPC with Linear Communication Overhead

Gilad Asharov (Bar Ilan University)

Thursday, 01.02.2024, 11:30

Taub 401

We study secure multiparty computation in the asynchronous setting with perfect security and optimal resilience (less than one-fourth of the participants are malicious). It has been shown that every function can be computed in this model [Ben-OR, Canetti, and Goldreich, STOC'1993].
Despite 30 years of research, all protocols in the asynchronous setting require $\Omega(n^2C)$ communication complexity for computing a circuit with $C$ multiplication gates. In contrast, for nearly 15...

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Almost Logarithmic Approximation for Cutwidth and Pathwidth

Dor Katzelnick

Wednesday, 31.01.2024, 12:30

Taub 201

We study several graph layout problems with a min max objective. Here, given a graph we wish to find a linear ordering of the vertices that minimizes some worst case objective over the natural cuts in the ordering; which separate an initial segment of the vertices from the rest. A prototypical problem here is cutwidth, where we want to minimize the maximum number of edges crossing a cut. The only known algorithm here is by [Leighton-Rao J.ACM 99] based on recursively partitioning ...

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Theory Seminar: Almost Logarithmic Approximation for Cutwidth and Pathwidth

Dor Katzelnick (Technion)

Wednesday, 31.01.2024, 12:15

Taub 201

We study several graph layout problems with a min max objective. Here, given a graph we wish to find a linear ordering of the vertices that minimizes some worst case objective over the natural cuts in the ordering; which separate an initial segment of the vertices from the rest. A prototypical problem here is cutwidth, where we want to minimize the maximum number of edges crossing a cut. The only known algorithm here is by [Leighton-Rao J.ACM 99] based on recursively partitioning ...

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ceClub: Challenges and Opportunities In Securing Software Supply Chains

Dr. Yaniv David (Columbia University)

Wednesday, 31.01.2024, 11:30

Meyer 861

Racing to be first to market and deploy new features, developers rely on many external libraries to underpin their software. Each library uses more libraries, creating vast networks of dependencies that the developers know little about and have no control over, forming a knowledge gap that quickly turns into technical debt. Repaying this debt is difficult, as analyzing or examining all libraries is infeasible, and worse, the debt keeps growing due to frequent library updates. Atta...

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CS Lecture: On Implicit Bias and Benign Overfitting in Neural Networks

Gal Vardi

Wednesday, 31.01.2024, 10:30

Taub 601

When training large neural networks, there are typically many solutions that perfectly fit the training data. Nevertheless, gradient-based methods often have a tendency to reach those which generalize well, namely, perform well also on test data. Thus, the training algorithm seems to be implicitly biased towards certain networks, which exhibit good generalization performance. Understanding this “implicit bias” has been a subject of extensive research recently. Moreover, in con...

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Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines

Michael Toker

Monday, 29.01.2024, 15:30

Taub 601

Text-to-image diffusion models (T2I) use a latent representation of a text prompt to guide the image generation process.
However, the encoder that produces the text representation is largely unexplored. We propose the Diffusion Lens, a method for analyzing the text encoder of T2I models by generating images from its intermediate representations. Using the Diffusion Lens, we perform an extensive analysis of two recent T2I models.We find that the text encoder gradually build...

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CS Lecture: Verification of Complex Hyperproperties

Hadar Frenkel

Sunday, 28.01.2024, 10:30

Auditorium 012, floor 0

Hyperproperties are system properties that relate multiple execution traces to one another. Hyperproperties are essential to express a wide range of system requirements such as information flow and security policies; epistemic properties like knowledge in multi-agent systems; fairness; and robustness. With the aim of verifying program correctness, the two major challenges are (1) providing a specification language that can precisely express the desired properties; and (2) providin...

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Theory Seminar: The Sample Complexity Of ERMs In Stochastic Convex Optimization

Daniel Carmon (Technion)

Wednesday, 24.01.2024, 12:15

Taub 201

Stochastic convex optimization is one of the most well-studied models for learning in modern machine learning. Nevertheless, a central fundamental question in this setup remained unresolved:
How many data points must be observed so that any empirical risk minimizer (ERM) shows good performance on the true population?
This question was proposed by Feldman who proved that Ω(\frac{d}{ϵ} + \frac{1}{ϵ^2}) data points are necessary (where d is the dimension and ε > 0 is the accurac...

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The Technion CTF Team opens at the Faculty

Tuesday, 23.01.2024, 18:30

Taub 9

The Technion CTF Team opens at the Faculty
We invite you to join the Capture The Flag - CTF meetings. CTF is a cyber challenge competition and information security on the topics: cryptography, reverse engineering, forensics, web, etc. The meetings will include guest lectures and practical experience in solving challenges.
Beginner and experienced students are welcome to join.
The first introductory meeting will be held on Tuesday, January 23 at 6:30 pm at Taub 9.
In the f...

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CGGC Seminar: Poisson Manifold Reconstruction (beyond co-dimension one)

Prof. Misha Kazhdan (Computer Science, Johns Hopkins University)

Thursday, 18.01.2024, 11:30

Taub 012 (Learning Center Auditorium)

In this talk we consider the problem of manifold reconstruction from oriented point clouds for embedded manifolds of co-dimension larger than one. Using the framework of Poisson Surface Reconstruction, and formulating the problem in the language of alternating products, we show that the earlier approach for reconstructing hyper-surfaces extends to general manifolds, at the cost of replacing a quadratic energy with a multi-quadratic energy. We provide an efficient iterative hierarc...

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Theory Seminar: Online edge coloring

David Wajc (Technion)

Wednesday, 17.01.2024, 12:15

Taub 201

Vizing’s Theorem provides an algorithm that edge colors any graph of maximum degree Δ using Δ+1 colors, which is necessary for some graphs, and at most one higher than necessary for any graph. In online settings, the trivial greedy algorithm requires 2Δ-1 colors, and Bar-Noy, Motwani and Naor in the early 90s showed that this is best possible, at least in the low-degree regime. In contrast, they conjectured that for graphs of superlogarithmic-in-n maximum degree, much better ...

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Internet-Scale Consensus in the Blockchain Era

Joachim Neu

Tuesday, 16.01.2024, 11:00

Taub 601

Blockchains have ignited interest in Internet-scale consensus as a vital building block for decentralized applications and services that promise egalitarian access and robustness to faults and abuse. While the study of consensus has a 40+ year tradition, the new Internet-scale setting requires a fundamental rethinking of models, desiderata, and protocols. An emergent key challenge is to simultaneously serve clients with different requirements regarding the two fundamental aspects ...

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12-Lead ECG Classification Using Deep Learning Methods

Vadim Gliner

Thursday, 04.01.2024, 12:30

Faculty of Biomedical Engineering, Silver Building, Room 201

12-lead electrocardiogram (ECG) recordings can be collected in any clinic and the interpretation is performed by a clinician. Modern machine learning tools may make them automatable. However, a large fraction of 12-lead ECG data is still available in printed paper or image only and comes in various formats. To digitize the data, smartphone cameras can be used. Nevertheless, this approach may introduce various artifacts and occlusions into the obtained images.Here, I will p...

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CS Lecture: Learning From Dependent Data And Its Modeling Through The Ising Model

Yuval Dagan

Monday, 01.01.2024, 10:30

Auditorium 012, floor 0

I will present a theoretical framework for analyzing learning algorithms which rely on dependent, rather than independent, observations. While a common assumption is that the learning algorithm receives independent datapoints, such as unrelated images or texts, this assumption often does not hold. An example is data on opinions across a social network, where opinions of related people are often correlated, for example as a consequence of their interactions. I will present a line o...

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