Speakers

  • Dr. Lex Fridman

    MIT

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  • Prof. Tal Arbel

    McGill University

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  • Dr. Nadav Cohen

    Tel Aviv University & Imubit

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  • Prof. Amir Globerson

    Tel Aviv University

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  • Prof. Michal Irani

    Weizmann Institute of Science

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  • Dr. Matan Protter

    Alibaba DAMO Israel Lab

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  • Tamar Rott Shaham

    Technion

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  • Prof. Shai Shalev-Shwartz

    Mobileye, Intel, Hebrew University

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Dr. Lex Fridman

ResearcherMIT

Bio:

Lex Fridman is a researcher at MIT, working on deep learning approaches in the context of semi-autonomous vehicles, human sensing, personal robotics, and more generally human-centered artificial intelligence systems. Before joining MIT, Lex was at Google working on machine learning for large-scale behavior-based authentication.

Title:

Deep Learning for Self-Driving Cars

Abstract:

I will present the state of the art in computer vision and deep learning methods for perception, prediction, planning, and human sensing in semi-autonomous and fully-autonomous vehicles. The talk will include the open problems in the field and ideas for approaches on how to solve them.

Prof. Tal Arbel

ProfessorMcGill University

Bio:

Tal Arbel is a Professor in the Department of Electrical and Computer Engineering, where she is the Director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines, McGill University. She is also an elected Associate Member of MILA (Montreal Institute for Learning Algorithms) and the Goodman Cancer Research Centre. Prof. Arbel’s research focuses on development of probabilistic machine learning methods in computer vision and medical image analysis, with a wide range of applications in neurology and neurosurgery. Her recent awards include receiving a Canada CIFAR AI Chair (2019), and the 2019 McGill Engineering Christophe Pierre Research Award. She regularly serves on the organizing team of major international conferences in both fields (e.g.  MICCAI, MIDL, ICCV, CVPR). She is currently an Associate Editor (AE) for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and is the Editor-in-Chief of a newly launched arXiv overlay journal: Machine Learning for Biomedical Imaging (MELBA).

Title:

Abstract:

Dr. Nadav Cohen

Asst. Professor of Computer Science at Tel Aviv University
Chief Scientist at Imubit
Tel Aviv University & Imubit

Bio:

Nadav Cohen is an Asst. Professor of Computer Science at Tel Aviv University, and Chief Scientist at Imubit.  His academic research revolves around the theoretical and algorithmic foundations of deep learning, while at Imubit he leads the development of deep learning systems controlling industrial manufacturing lines.  Nadav earned a BSc in electrical engineering and a BSc in mathematics (both summa cum laude) at the Technion Excellence Program for Distinguished Undergraduates.  He obtained his PhD (direct track, summa cum laude) at the Hebrew University, and was subsequently a postdoctoral scholar at the Institute for Advanced Study in Princeton.  For his contributions to deep learning, Nadav won a number of awards, including the Google Doctoral Fellowship in Machine Learning, the Final Prize for Machine Learning Research, the Rothschild Postdoctoral Fellowship, the Zuckerman Postdoctoral Fellowship, and TheMarker's 40 under 40 list.

Title:

Practical Implications of Theoretical Deep Learning

Abstract:

Deep learning is experiencing unprecedented success in recent years, delivering state of the art performance in numerous application domains.  However, despite its extreme popularity and the vast attention it is receiving, this technology suffers from various limitations --- in terms of stability, reliability, explainability and more --- hindering its proliferation.  In this talk, I will argue that theoretical analyses of deep learning may assist in addressing such limitations, by providing principled tools for neural architecture and optimization algorithm design.  Two examples will be given: (i) application of tensor analysis and quantum mechanics for configuring the architecture of a convolutional neural network; and (ii) dynamical analysis of gradient descent over linear neural networks for enhancing convergence and generalization properties.

Prof. Amir Globerson

Professor, The Blavatnik School of Computer ScienceTel Aviv University

Bio:

Holds a BSc in computer science and physics, and a PhD in computational neuroscience. After his PhD, he was a postdoctoral fellow at the University of Toronto and a postdoctoral fellow at MIT. His research interests include machine learning, deep learning, graphical models, optimization, machine vision, and natural language processing. His work has received several prizes including five paper awards at NeurIPS, ICML and UAI. In 2019, he received the ERC Consolidator Grant.

Title:

Generating Scene Graphs from Images and Images from Scene Graphs

Abstract:

Prof. Michal Irani

Professor Weizmann Institute of Science

Bio:

Michal Irani is a Professor at the Weizmann Institute of Science, in the Department of CS and Applied Mathematics. She received her PhD from the Hebrew University (1994), and joined the Weizmann Institute in 1997. Her research interests center around Computer-Vision, Image-Processing, AI and Video information analysis. Michal's recent prizes and honors include the Maria Petrou Prize (2016), the Helmholtz “Test of Time Award” (2017), the Landau Prize for Arts & Sciences (2019), and the Rothschild Prize (2020). She also received the ECCV Best Paper Award in 2000 and in 2002, and was awarded the Honorable Mention for the Marr Prize in 2001 and in 2005.

Title:

“Deep Internal learning” -- Deep Learning with Zero Examples

Abstract:

I will show how complex visual inference can be performed with Deep-Learning, in a totally unsupervised way, by training on a single image -- the test image itself. The strong recurrence of information inside a single image provides powerful internal examples, which suffice for self-supervision of CNNs, without any prior examples or training data. This gives rise to true “Zero-Shot Learning”. I will show the power of this approach to a variety of problems, including super-resolution, segmentation, transparency separation, dehazing, image-retargeting, and more.

I will further show how self-supervision can be used for “Mind-Reading” (reconstructing images from fMRI brain recordings), despite having only little training data.

Dr. Matan Protter

Associate DirectorAlibaba DAMO Israel Lab

Bio:

Matan is leading the eXtended Reality (XR) efforts in Alibaba DAMO Israel Lab. He was previously the CTO and co - founder of Infinity Augmented Reality, which developed AR glasses and was acquired by Alibaba in 2019. He has been working in various computer vision fields for over 15 years.  Matan holds a PhD (direct program) in Computer Science from the Technion (2010) and is an alumni of Talpiot program. 

Title:

Full Computer Vision Stack @ Alibaba

Abstract:

Gone are the days when we researchers would spend years specializing in only one computer vision field. In this talk, we will show how we are combining the gamut of CV tasks, from classification and segmentation to 3D and GANs, using data (both real and synthetic) to solve real-world e-commerce challenges in Alibaba’s scale. As an example, we will detail how we effectively train, combine and deploy all of these varied tasks in the context of a Home Decor e-retail project.

Tamar Rott Shaham

PhD candidate, EE FacultyTechnion

Bio:

Tamar Rott Shaham is a PhD candidate at the Electrical Engineering faculty in the Technion - Israel Institute of Technology, under the supervision of Prof. Tomer Michaeli, where she also received her B.Sc. in 2015. Her research interests are in Image Processing and Computer Vision. Tamar won several awards including Adobe Research Fellowship (2020), ICCV 2019 Best Paper Award (Marr Prize), Google WTM Scholar (2019), The Israeli Higher Education Council Scholarship for Data Science PhD students, and the Schmidt Postdoctoral Award.

Title:

SinGAN: Learning a Generative Model from a Single Natural Image

Abstract:

We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.

Prof. Shai Shalev-Shwartz

Chief Technology Officer, Mobileye
Senior Fellow, Intel Corporation
Professor at the Rachel and Selim Benin School of Computer Science and Engineering at the Hebrew University of Jerusalem
Mobileye, Intel, Hebrew University

Bio:

Shai Shalev-Shwartz is the CTO of Mobileye, a Senior Fellow at Intel.

Professor Shalev-Shwartz holds a professor position in the Rachel and Selim Benin School of Computer Science and Engineering at the Hebrew University of Jerusalem. Before joining Hebrew University, Prof. Shalev-Shwartz was a research assistant professor at Toyota Technological Institute in Chicago, as well as having worked at Google and IBM research. Prof. Shalev-Shwartz is the author of the book “Online Learning and Online Convex Optimization,” and a co-author of the book “Understanding Machine Learning: From Theory to Algorithms.” Prof. Shalev-Shwartz has written more than 100 research papers, focusing on machine learning, online prediction, optimization techniques, and practical algorithms.

Title:

On the Challenges of Building a Camera-only, Complete, Self-Driving System

Abstract:

Humans can drive a car using a vision-only system, without relying on 3D sensors at all, and achieve a remarkable high accuracy. Can we match this ability using computer vision? The talk will focus on some of the challenges, including machine learning with extremely high accuracy, lifting a 2D projection back to the 3D world, and developing decision-making algorithms that are robust to sensing errors.