Program

Dr. Leonid Karlinsky
DL Team Lead, CVAR group
IBM Research AI

Bio

Dr. Karlinsky leads the CV & DL research team in the Computer Vision and Augmented Reality (CVAR) group @ IBM Research AI. He is a Computer Vision and Machine Learning expert with years of hands on experience. He is actively publishing research papers in leading CV and ML venues such as ECCV, CVPR and NIPS and is actively reviewing for these conferences for the past 10 years. Dr. Karlinsky holds a PhD degree in CV from the Weizmann Institute of Science, supervised by Prof. Shimon Ullman.

Abstract

Learning to classify and localize instances of objects that belong to new categories, while training on just one or very few examples, is a long-standing challenge in modern computer vision. This problem is generally referred to as 'few-shot learning'. It is particularly challenging for modern deep-learning based methods, which tend to be notoriously hungry for training data.  In this talk I will cover several of our recent research papers offering advances on these problems using example synthesis (hallucination) and metric learning techniques and achieving state-of-the-art results on known and new few-shot benchmarks. In addition to covering the relatively well studied few-shot classification task, I will show how our approaches can address the yet under-studied few-shot localization and multi-label few-shot classification tasks. In addition to this talk, a detailed tutorial covering the few-shot learning field in general will be given by Dr. Joseph Shtok from my team.

Oron Nir
Senior Data Scientist
Microsoft Media AI research team

Bio

Oron is a founding member of the Microsoft Video Indexer, a media analytics cloud service recently announced GA, and a Senior Data Scientist at the Microsoft Media AI research team. He has acquired his B.Sc. and M.Sc. degrees at the Ben-Gurion University of the Negev, with a thesis at the field of Computer Vision.

Abstract

Media companies accumulate large archives and struggle to transform it into business value. Media is unstructured and therefore hard to manage at scale. Content categorization by topics is an intuitive approach that makes it easier for people to search. Many organizations turn to tagging their content manually, which is expensive and isn’t scalable. To automate this process, we apply multimodal topic detection and tracking (TDT). The information communicated through the speech, OCR, and identified celebrities is detected using Named Entity Recognition, clustered by concepts into similarity graphs, named using Wikipedia Categories ranker in an interpretable manner with evolvement over time.

Soliman Nasser
Lead Research and Development Engineer
Blink Technologies Inc.

Bio

Soliman Nasser is a Computer Scientist and Researcher, working as a Lead Research and Development Engineer at Blink Technologies Inc. Previously, Soliman was a part of the Algorithms team at Intel RealSense, developing depth cameras 3D core algorithms. Soliman completed his B.Sc in tandem with high school studies as a part of "Etgar" Program, and M.Sc in Computer Science under the supervision of Dr. Dan Feldman at the Robotics and Big Data Lab, both from the University of Haifa. Soliman's research interests include: Computer Vision, Multiple View Geometry, {Machine, Deep}-Learning, Vision for Robotics, SLAM, Depth cameras , Eye-Tracking and more.

Abstract

The eyes play a vital role in the perception of our evolving surrounding as well as in communication with other human beings. Apart from the behavioral and scientific research in medical diagnoses
and psychological studies, estimating gaze direction is also important in Mixed Reality, AI assistant devices, Automotive, Human-Computer and Robot interaction, User authentication and many other applications.
In this talk, I will give a short introduction to eye-tracking research, compare between the geometric (model based) and data-driven (appearance based) methods, with emphasis on the ad-vantages and disadvantages of deep-learning in this domain and present some interesting usages of this technology.

Deep learning has recently become an abundant technology for analyzing video data. However, the increasing resolution and frame rates of videos make real-time analysis a remarkably challenging task.
We present an overview of a novel architecture based on Redis, Docker, and TensorFlow that enables real-time analysis of high-resolution streaming video. The solution can serve advanced deep learning algorithms at subsecond rates and appears fully synchronous to the user despite containing an asynchronous backend. The talk offers a demo using visual inspection and shares results that highlight the solution’s applicability to real-time neural network processing of videos. The approach is generalizable and can be applied to diverse domains that require video analytics.

Augmented Reality is the combination of Computer Vision and Computer Graphics. As technology mature and is now being commoditized, new innovation can emerge. In this talk, we would share our story of Augmented Reality @ Trax. How we use it for multi-perspective geometry, indoor mapping and navigation, and even challenging the shopping experience.

Yonatan Cohen, Ph.D.
CTO
Quantum Machines

Bio

I completed my PhD in Physics at the Weizmann Institute in professor Moty Heiblum's group, where I investigated quantum electronics devices and topological quantum states. 

A year ago, together with Itamar Sivan and Nissim Ofek, I co-founded Quantum Machines (QM), which is the first Israeli startup in quantum computing. At QM we develop control and operation systems for quantum computers. With a multidisciplinary team of highly motivated physicists and engineers, all passionate about solving the challenges of quantum control, we hope to help making the dream of a large-scale quantum computer become a reality. 

Abstract

Quantum mechanics, which was born at the beginning of the 20th century and revolutionized our understanding of nature, implies that nature is far more counter intuitive and far richer than anyone could imagine. In the 1980’s, physicists understood that this richness of nature can be used to construct new computers that can outperform any classical computer. In the decades that followed, physicists have made huge progress in designing and controlling quantum systems, and today quantum computers seem to be closer than ever. What are the basic principles of quantum computers? how do they allow them to beat classical computers? What problems will they be best for? 

Eldad Klaiman
Principal Scientist
Roche Innovation Center Munich

Bio

B.Sc and M.Sc in electrical engineering from the Technion IIT in Haifa. For the past 4 years working as a principal scientist at the Roche Innovation Center near Munich, Germany in the Oncology division of Roche Pharma Research and Early Development. During this period he has been responsible for the development and application of machine learning algorithms, mostly in the field of pathology tissue analytics and leveraging Roche's internal datasets in order to develop novel technology and techniques for informing and supporting oncology drug development.

Abstract

Modern day pharma organizations face mounting challenges in the field of drug research and development due to the pharma competitive landscape, the compelxity of treatment methods and increasing number of drug combination studies. At Roche we are persuing a Personalized Healthcare (PHC) approach to drug development, which is meant to match the best medicine to each specific patient. This approach has the potential to drastically improve drug efficacy for our patients but it also poses the challenge of patient-drug selection. The ammount of data available in the drug development process, from histology and clinical imaging, from genomics and sequencing, from real world data and from diagnostic methods present a unique oportunity for the extraction of novel insights and the development of new technologies. These novel machine learning techniques are helping us address these challenges and leverage this information to assist scientists in the research process, supporting descision making and bringing better drugs to patients, faster. 

Dr. Joseph Shtok
Research Staff Member at Computer Vision and Augmented Reality Team
IBM Research AI, Haifa Lab

Bio

Joseph Shtok holds an M.Sc. degree in Mathematics and a Ph.D. degree in Computer Science, from the Technion – Israel Institute of Technology. His areas of expertise include Machine Learning, Computer Vision with focus on Object Recognition, Image Processing, Medical Imaging and depth sensors. Joseph is one of the leading research scientists of the IBM Research AI team, specializing in object recognition and 3D algorithms. His research papers are published in leading Computer Vision and Signal Processing conferences such as CVPR, NIPS, ISBI, ICASSP.

Abstract

Few-shot learning is a rapidly evolving field in computer vision, where the standard tasks of classification and detection are addressed in a challenging scenario with only few examples per class available for training. Using the approaches of meta-learning, metric learning, synthesis (hallucination) and data augmentation, substantial progress has been made in this field during the recent years. We will provide an overview of the state of the art in few-shot learning, highlighting and describing the main methods and results in the aforementioned directions.

Adham Ghazali
CEO & Co-Founder
​Imagry

Bio

Adham Ghazali is the CEO of Imagry. He spent the last 10 years working on various machine learning problems including large scale computer vision, Brain computer interfacing and Bio-Inspired facial recognition. He is interested in the intersection between biology and computer science. At his current post, he is responsible for strategic R&D and Business Development.  
Prior to cofounding Imagry, Adham was a brain researcher focusing on the study of the visual system in infants. 

Abstract

Autonomous driving has come a long way albeit it's success is limited only to those areas where a High-Definition map is pre-built and known. Inspired by the recent achievements in Artificial Intelligence particularly of methods that combine trees and DNNs (e.g. Alpha Go Zero), this talk demonstrates how to effectively combine Deep Learning and convention path planning to drive in unknown areas. 

Dr. Moti Freiman
Staff Research Scientist
Global Advanced Technology, CT/AMI
Philips Healthcare

Bio

Moti Freiman is a staff research scientist at Philips Healthcare where he is developing advanced algorithms with the aim of improving the capacity of medical imaging devices to provide clinically meaningful information by leveraging machine learning, computer vision and image processing algorithms.

Prior to Philips, Dr. Freiman was an Instructor in Radiology at Harvard Medical School where he developed advanced algorithms for quantitative analysis of diffusion-weighted MRI data.

Dr. Freiman is the recipient of an NIH R01 research grant and the 2012 Crohn's and Colitis foundation of America research fellow award. He is the author and co-author of more than 40 journal and full-length conference papers and holds several patents and patent applications.

Abstract

Deep sparse auto-encoders with mixed structure regularization (MSR) in addition to explicit sparsity regularization term and stochastic corruption of the input data with Gaussian noise have the potential to improve unsupervised abnormality detection. Unsupervised abnormality detection based on identifying outliers using deep sparse auto-encoders is a very appealing approach for medical computer aided detection systems as it requires only healthy data for training rather than expert annotated abnormality. 

In the task of detecting coronary artery disease from Coronary Computed Tomography Angiography (CCTA), our results suggests that the MSR has the potential to improve overall performance by 20-30% compared to deep sparse and denoising auto-encoders.

Sagie Benaim
PhD candidate
Tel Aviv University

Bio

A PhD candidate under the supervision of Prof. Lior Wolf at Tel Aviv University. Sagie has a several publications on GANs and related models, and its his main domain expertise.

 Abstract

The talk will focus and the recent advances in Generative Adversarial Networks (GANs) for Image to Image Translations. I will begin by describing the problem and the distinction between supervised and unsupervised approaches, focusing on the approaches of pix2pix, DTN, CycleGAN, Unit and DistanceGAN. I will then describe recent approaches which allow for variation in the target domain as well as "one-shot"-translation. Lastly, I will talk about the application of these techniques in other domains such as audio and text.