When

17th & 18th October, 2017 08:30 am - 07:00 pm

Website: GTC Israel 2017

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Where

Tel Aviv Convention Center, Building 2
Rokach Blvd. 101
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  Sessions   Grid   List

Tuesday, 17th October 2017

Time Hall D Hall E Hall F Hall G Hall H Hall I Hall KLM Hall C
8:30 am Registration Open
by GTC Israel
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Registration Open

By:
GTC Israel
October 17, 2017, 8:30 am to 10:30 am
Hall: Hall D
9:00 am
9:30 am
10:00 am
10:30 am     Introduction and Integration with DriveWorks on DRIVE PX2**
by Aaraadhya Narra (NVIDIA, Solutions Architect)
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Introduction and Integration with DriveWorks on DRIVE PX2**

Introduction and Integration with DriveWorks on DRIVE PX2**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

NVIDIA DriveWorks is a Software Development Kit (SDK) that contains reference applications, tools and library modules. It also includes a run-time pipeline framework that goes from detection to localization to planning to visualization. It is designed to be educational to use and open so you can enhance it with your own code. This lab session introduces DriveWorks by running the demos which showcase the available modules. You will learn how to integrate sensors using the Sensor Abstraction Layer provided by DriveWorks, followed by the integration of DriveWorks modules into your custom code or applications. Pre-requisite for this lab include Basic Linux, C/C++ programming and deep learning knowledge.

By:
Aaraadhya Narra (NVIDIA, Solutions Architect)
October 17, 2017, 10:30 am to 12:30 pm
Hall: Hall F Track: Deep Learning Institute Labs Type: Instructor Led Lab
      Image Classification with DIGITS**
by Jonas Lööf (NVIDIA, Solutions Architect)
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Image Classification with DIGITS**

Image Classification with DIGITS**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

Learn how to leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS. You will walk through the process of data preparation, model definition, model training and troubleshooting. You will use validation data to test and try different strategies for improving model performance using GPUs. On completion of this lab, you will be able to use DIGITS to train a DNN on your own image classification application.

By:
Jonas Lööf (NVIDIA, Solutions Architect)
October 17, 2017, 10:30 am to 12:30 pm
Hall: Hall KLM Track: Deep Learning Institute Labs Type: Instructor Led Lab
 
11:00 am            
11:30 am            
12:00 pm            
12:30 pm             Lunch  
1:00 pm              
1:30 pm     Introduction and Integration with DriveWorks on DRIVE PX2**
by Aaraadhya Narra (NVIDIA, Solutions Architect)
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Introduction and Integration with DriveWorks on DRIVE PX2**

Introduction and Integration with DriveWorks on DRIVE PX2**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

NVIDIA DriveWorks is a Software Development Kit (SDK) that contains reference applications, tools and library modules. It also includes a run-time pipeline framework that goes from detection to localization to planning to visualization. It is designed to be educational to use and open so you can enhance it with your own code. This lab session introduces DriveWorks by running the demos which showcase the available modules. You will learn how to integrate sensors using the Sensor Abstraction Layer provided by DriveWorks, followed by the integration of DriveWorks modules into your custom code or applications. Pre-requisite for this lab include Basic Linux, C/C++ programming and deep learning knowledge.

By:
Aaraadhya Narra (NVIDIA, Solutions Architect)
October 17, 2017, 1:30 pm to 3:30 pm
Hall: Hall F Track: Deep Learning Institute Labs Type: Instructor Led Lab
      Object Detection with DIGITS**
by Jonas Lööf (NVIDIA, Solutions Architect)
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Object Detection with DIGITS**

Object Detection with DIGITS**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

This lab explores three approaches to identify a specific feature within an image. Each approach is measured in relation to three metrics: model training time, model accuracy and speed of detection during deployment. On completion of this lab, you will understand the merits of each approach and learn how to detect objects using neural networks trained on NVIDIA DIGITS using real-world datasets.

By:
Jonas Lööf (NVIDIA, Solutions Architect)
October 17, 2017, 1:30 pm to 3:30 pm
Hall: Hall KLM Track: Deep Learning Institute Labs Type: Instructor Led Lab
 
2:00 pm            
2:30 pm            
3:00 pm            
3:30 pm             Break  
4:00 pm     Introduction and Integration with DriveWorks on DRIVE PX2**
by Aaraadhya Narra (NVIDIA, Solutions Architect)
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Introduction and Integration with DriveWorks on DRIVE PX2**

Introduction and Integration with DriveWorks on DRIVE PX2**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

NVIDIA DriveWorks is a Software Development Kit (SDK) that contains reference applications, tools and library modules. It also includes a run-time pipeline framework that goes from detection to localization to planning to visualization. It is designed to be educational to use and open so you can enhance it with your own code. This lab session introduces DriveWorks by running the demos which showcase the available modules. You will learn how to integrate sensors using the Sensor Abstraction Layer provided by DriveWorks, followed by the integration of DriveWorks modules into your custom code or applications. Pre-requisite for this lab include Basic Linux, C/C++ programming and deep learning knowledge.

By:
Aaraadhya Narra (NVIDIA, Solutions Architect)
October 17, 2017, 4:00 pm to 6:00 pm
Hall: Hall F Track: Deep Learning Institute Labs Type: Instructor Led Lab
      Neural Network Deployment with DIGITS and TensorRT**
by Jonas Lööf (NVIDIA, Solutions Architect)
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Neural Network Deployment with DIGITS and TensorRT**

Neural Network Deployment with DIGITS and TensorRT**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

This lab will show three approaches for deployment. The first approach is to directly use inference functionality within a deep learning framework, in this case NVIDIA DIGITS and Caffe. The second approach is to integrate inference within a custom application by using a deep learning framework API, again using Caffe, but this time through its Python API. The final approach is to use the NVIDIA TensorRT™, which will automatically create an optimized inference run-time from a trained Caffe model and network description file. In this lab, you will learn about the role of batch size in inference performance, as well as various optimizations that can be made in the inference process. You will also explore inference for a variety of different DNN architectures trained in other DLI labs.

By:
Jonas Lööf (NVIDIA, Solutions Architect)
October 17, 2017, 4:00 pm to 6:00 pm
Hall: Hall KLM Track: Deep Learning Institute Labs Type: Instructor Led Lab
 
4:30 pm            
5:00 pm            
5:30 pm            
6:00 pm                
6:30 pm                

Wednesday, 18th October 2017

Time Hall D Hall E Hall F Hall G Hall H Hall I Hall KLM Hall C
8:30 am Registration Open & Welcome Reception
by NVIDIA
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Registration Open & Welcome Reception

By:
NVIDIA
October 18, 2017, 8:30 am to 10:00 am
Hall: Hall D Type: Special Event
9:00 am
9:30 am
10:00 am Opening Keynote
by Jensen Huang (NVIDIA, Founder & CEO)
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Opening Keynote

Don’t miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing and highlights the impact of artificial intelligence and deep learning.

By:
Jensen Huang (NVIDIA, Founder & CEO)
October 18, 2017, 10:00 am to 12:00 pm
Hall: Hall D Track: General Deep Learning Type: Keynote
10:30 am
11:00 am
11:30 am
12:00 pm Lunch at Hall C Exhibits and VR Village Open
by GTC Israel
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Exhibits and VR Village Open

Come experience the latest demos from NVIDIA and GTC Israel sponsors, along with startups on the brink of transforming the world.

By:
GTC Israel
October 18, 2017, 12:00 pm to 5:30 pm
Hall: Hall C Track: Solutions Type: Special Event
12:30 pm
1:00 pm Using DRIVE PX To Build Self-Driving Vehicles
by Gaurav Agarwal (NVIDIA, Sr. Product Manager)
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Using DRIVE PX To Build Self-Driving Vehicles

Revised description: We'll cover how to use NVIDIA DRIVE PX to power a self-driving vehicle. This will include insights into how DRIVE PX is used to acquire data, which in turn is used to train different types of Neural Networks. And, finally how DRIVE PX is used as the brain of the autonomous vehicle to run AV applications utilizing these neural networks in real-time. We will also focus on the types of sensors required to perceive the driving environment and various tools necessary to build an AV application.

By:
Gaurav Agarwal (NVIDIA, Sr. Product Manager)
October 18, 2017, 1:00 pm to 1:30 pm
Hall: Hall D Track: Auto Type: Talk
AI Day for VCs (Invite Only)
by Jeff Herbst (NVIDIA, Vice President of Business Development)
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AI Day for VCs (Invite Only)

This is a special invite-only event for Venture Capitalists. To learn more and apply for access, visit https://www.nvidia.com/en-il/gtc/sessions/ai-startups/.

By:
Jeff Herbst (NVIDIA, Vice President of Business Development)
October 18, 2017, 1:00 pm to 3:00 pm
Hall: Hall E Track: General Deep Learning Type: Special Event
Deep Learning: An Artificial Brain That Detects Any Type of Cyber Threat
by Eli David (Deepinstinct, CTO)
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Deep Learning: An Artificial Brain That Detects Any Type of Cyber Threat

Join our presentation on the first application of deep learning to cybersecurity. Deep learning is inspired by the brain's ability to learn: once a brain learns to identify an object, its identification becomes second nature. Similarly, as a deep learning-based artificial brain learns to detect any type of cyber threat, its prediction capabilities become instinctive. As a result, the most evasive and unknown cyber-attacks are immediately detected and prevented. We will cover the evolution of artificial intelligence, from old rule-based systems to conventional machine learning models until current state-of-the-art deep learning models.

By:
Eli David (Deepinstinct, CTO)
October 18, 2017, 1:00 pm to 1:30 pm
Hall: Hall F Track: Cyber Security Type: Talk
NVIDIA Metropolis: The Foundation of AI-Enabled Cities
by Deepu Talla (NVIDIA, Vice President and General Manager)
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NVIDIA Metropolis: The Foundation of AI-Enabled Cities

Smart and safe cities need AI. There are approximately 500 million cameras deployed globally today. When it comes to analyzing that data, traditional methods of video analytics often fall short. AI and deep learning can provide the level of accuracy needed to extract meaningful real-time insights. The result is improved public safety and more efficient city operations. NVIDIA Metropolis is the company’s edge-to-cloud platform for the AI City. It includes solutions for deep learning at the edge, in on-prem servers and in the cloud, as well as a comprehensive SDK. During this talk, we’ll provide an overview on NVIDIA Metropolis, its different applications, and its critical role in the creation and expansion of smart and safe cities.

By:
Deepu Talla (NVIDIA, Vice President and General Manager)
October 18, 2017, 1:00 pm to 1:30 pm
Hall: Hall G Track: Smart Cities Type: Talk
  From Data to Diagnostic Algorithms
by Eyal Toledano (Zebra Medical Vision, Co-Founder, CTO)
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From Data to Diagnostic Algorithms

We will deep dive into the challenges of data acquisition and present our suite of clinical tools that build neural networks to cope with the complexities of 3D modalities and high resolution images.

By:
Eyal Toledano (Zebra Medical Vision, Co-Founder, CTO)
October 18, 2017, 1:00 pm to 1:30 pm
Hall: Hall I Track: Healthcare Type: Talk
Modeling Time Series Data with Recurrent Neural Networks in Keras**
by Gunter Roeth (NVIDIA, Solutions Architect)
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Modeling Time Series Data with Recurrent Neural Networks in Keras**

Modeling Time Series Data with Recurrent Neural Networks in Keras**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

One important area of current research is the use of deep neural networks to classify or forecast time-series data. Time-series data is produced in large volumes from sensors in a variety of application domains including Internet of Things (IoT), cyber security, data center management and medical patient care. In this lab, you will learn how to create training and testing datasets using electronic health records in HDF5 (hierarchical data format version five) and prepare datasets for use with recurrent neural networks (RNNs), which allows modeling of very complex data sequences. You will then construct a long-short term memory model (LSTM), a specific RNN architecture, using the Keras library running on top of Theano to evaluate model performance against baseline data.

By:
Gunter Roeth (NVIDIA, Solutions Architect)
October 18, 2017, 1:00 pm to 3:00 pm
Hall: Hall KLM Track: Deep Learning Institute Labs Type: Instructor Led Lab
1:30 pm Generalizing Driving with Reinforcement Learning on top of Direct Perception
by Adham Ghazali (Imagry, CEO)
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Generalizing Driving with Reinforcement Learning on top of Direct Perception

Direct Perception (DP) is a known approach for autonomous driving in which a monolithic convolutional neural network is used to extract the most useful driving-parameters from raw images. We use a similar new approach that extracts abstract model of the road called Model Perception (MP), and then use continuous control with deep reinforcement learning (DDPG) to train a driving agent on top of this model. The driving agent controls both steering and throttle, it develops a safe and smooth driving behavior, and we show that it can drive on never-before seen tracks, all while using only images as input. Imagry is developing a cameras-only level 4/5 self-driving platform that amounts to a fraction of the cost of traditional LiDar, Radar & HD GPS-based solutions. The AI technology is based on Deep Inverse Reinforcement Learning algorithms which accelerate the training and performance of its unified software solution (end-to-end perception, planning, and control), especially in complex unseen scenarios.

By:
Adham Ghazali (Imagry, CEO)
October 18, 2017, 1:30 pm to 2:00 pm
Hall: Hall D Track: Auto
Cybersecurity for Self-Driving Cars: Staying Connected and Protected
by Monique Lance (Argus Cyber Security, Director of Marketing)
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Cybersecurity for Self-Driving Cars: Staying Connected and Protected

With rapid advances in connectivity, advanced driving systems and the accelerated trend towards self-driving cars, the automotive ecosystem is changing. These trends promise great benefits for motorists and commercial fleets. However, they also pose a significant increase in cyber risk and threaten consumer trust in vehicles. Today, more than ever cyber security is becoming integral to road safety and almost all major brands have been attacked. Unlike safety features, cyber security involves both prevention mechanisms and ongoing vigilance throughout the vehicle's lifetime-from the concept stage till the vehicle's decommission. For automakers, this requires adopting a holistic cyber security approach and incorporating procedures and requirements into their corporate strategy. In this presentation you will hear about measures that will help the automotive industry prevent, understand and respond to cyber threats so as to maintain and promote consumer trust in modern transportation.

By:
Monique Lance (Argus Cyber Security, Director of Marketing)
October 18, 2017, 1:30 pm to 2:00 pm
Hall: Hall F Track: Cyber Security Type: Talk
Disrupting Vehicle Inspection Using Deep Learning
by Ilya Bogomolny (UVeye, Lead Algorithms Developer)
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Disrupting Vehicle Inspection Using Deep Learning

We will describe a fast and accurate AI-based GPU accelerated Vehicle inspection system which scans the underside of moving vehicles to identify threatening objects or unlawful substances (bombs, unexposed weapons and drugs), vehicle leaks, wear and tear, and any damages that would previously go unnoticed.

By:
Ilya Bogomolny (UVeye, Lead Algorithms Developer)
October 18, 2017, 1:30 pm to 2:00 pm
Hall: Hall G Track: Smart Cities Type: Talk
DGX Systems: Best Practices for Deep Learning from Desk to Data Center
by Joshua Patterson (NVIDIA, Applied Solutions Engineering Director)
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DGX Systems: Best Practices for Deep Learning from Desk to Data Center

NVIDIA DGX Systems powered by Volta deliver breakthrough performance for today's most popular deep learning frameworks. Attend this session to hear from DGX product experts and gain insights that will help researchers, developers, and data science practitioners accelerate training and iterate faster than ever. Learn (1) best practices for deploying an end-to-end deep learning practice, (2) how the newest DGX systems including DGX Station address the bottlenecks impacting your data science, and (3) how DGX software including optimized deep learning frameworks give your environment a performance advantage over GPU hardware alone.

By:
Joshua Patterson (NVIDIA, Applied Solutions Engineering Director)
October 18, 2017, 1:30 pm to 2:00 pm
Hall: Hall H Track: General Deep Learning Type: Talk
Data-Driven Innovation in Health Policy and Healthcare Practice
by Ran Balicer (Clalit Research Institute, Director)
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Data-Driven Innovation in Health Policy and Healthcare Practice

Health systems worldwide need greater availability and intelligent integrated use of data and information technology. Clalit has been leading innovative interventions using clinical data to drive people-centered targeted and effective care models, for chronic disease prevention and control. Clalit actively pursues a paradigm shift to properly deal with these challenges, using IT, data and advanced analytics to transform its healthcare system to one which can bridge the silos of care provision in a patient-centered approach, and move from reactive therapeutic to proactive preventive care. In the presentation we will detail specific examples that allowed for reducing healthcare disparities, preventing avoidable readmissions, and improving control of key chronic diseases.

By:
Ran Balicer (Clalit Research Institute, Director)
October 18, 2017, 1:30 pm to 2:00 pm
Hall: Hall I Track: Healthcare Type: Talk
2:00 pm Fusing Vision and 3D Sensors with AI to Build Cognition Systems
by Youval Nehmadi (VayaVision, CTO)
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Fusing Vision and 3D Sensors with AI to Build Cognition Systems

In this session we will present how our sensor fusion and solution architecture, running on the DRIVE PX 2 platform, provides the advanced perception needed for self-driving cars. The raw data fusion of camera and LiDAR generates a high resolution 3D RGBd model. In combination with AI, it detects even small obstacles such as a torn tire and rocks on the road, much better than standard classification methods such as Deep Neural Network semantic segmentation. We will also show examples of sensor fusion cognition as compared to the typical high level fusion implementation.

By:
Youval Nehmadi (VayaVision, CTO)
October 18, 2017, 2:00 pm to 2:30 pm
Hall: Hall D Track: Auto Type: Talk
Inference in the DC on Tesla
by Aaraadhya Narra (NVIDIA, Solutions Architect)
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Inference in the DC on Tesla

In Deep Learning, Inference is where neural networks deliver insights. What started with images has quickly expanding to include speech, NLP, recommender systems and video. As data sets get bigger, networks get deeper and more complex, and latency requirements get tighter, GPUs are the ideal platform to accelerate these workloads, both for high batch and low-latency use-cases. In this talk, you'll learn how inference gets done on GPUs, get the latest on updates on the software stack for Inference including TensorRT inference engine and DeepStream SDK and recommendations on choosing the right GPU for running inference workloads.

By:
Aaraadhya Narra (NVIDIA, Solutions Architect)
October 18, 2017, 2:00 pm to 2:30 pm
Hall: Hall F Track: General Deep Learning Type: Talk
Leveraging Deep Learning to Transform Video Data into Actionable Intelligence
by Tom Edlund (Briefcam, CTO)
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Leveraging Deep Learning to Transform Video Data into Actionable Intelligence

Video is increasingly becoming a key sensor for maintaining security, business performance and efficient operations. This session will discuss the technology and application of BriefCam's video analytics solutions. Topics will include how GPUs and deep learning generates rich metadata from video and how it solves a diverse range of problems and applications.

By:
Tom Edlund (Briefcam, CTO)
October 18, 2017, 2:00 pm to 2:30 pm
Hall: Hall G Track: Smart Cities Type: Talk
An Introduction to the AI Services at AWS (Presented by Amazon Web Services)
by Boaz Ziniman (AWS, Technical Evangelist)
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An Introduction to the AI Services at AWS (Presented by Amazon Web Services)

Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.

By:
Boaz Ziniman (AWS, Technical Evangelist)
October 18, 2017, 2:00 pm to 2:30 pm
Hall: Hall H Track: General Deep Learning Type: Talk
Identifying Hundreds of Genetic Disorders Using Deep Learning
by Yaron Gurovich (FDNA, VP R&D)
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Identifying Hundreds of Genetic Disorders Using Deep Learning

In this talk, FDNA will present how deep learning is used to build an applicable framework that is used to aid in identification of hundreds of genetic disorders and help kids all over the world. Genetic Disorders affect one in every ten people. Many of these diseases are characterized by observable traits of the affected individuals - a 'phenotype'. In many cases, this phenotype is especially noticeable in the facial features of the patients, Down syndrome for example. But most such conditions have subtle facial patterns and are harder to diagnose. FDNA will describe their solution, its ability to generalize well for hundreds of Disorders while learning from a small amount of images per class, and its application for genetic clinicians and researchers.

By:
Yaron Gurovich (FDNA, VP R&D)
October 18, 2017, 2:00 pm to 2:30 pm
Hall: Hall I Track: Healthcare Type: Talk
2:30 pm Teaching a Car to Drive
by Larry Jackel (NVIDIA, Deep Learning and Robotics Specialist)
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Teaching a Car to Drive

NVIDIA's Autonomous Vehicle Research Lab will present breakthroughs in developing and testing deep neural networks to improve the safety and robustness of self-driving cars. One approach involves teaching a deep convolutional neural network (DNN) to drive by observing human drivers and emulating their behavior for lane keeping, lane changes, and turns. In addition, the session will showcase tools used to visualize the data processing of the neural network during training and testing, as well as the use of simulation to enhance the training process. This technology is part of an end-to-end platform that will ultimately enable self-driving cars up to Level 5. Finally, the session will cover how DNNs can learn autonomous driving related tasks that were previously thought solvable only by manual decomposition of the problem, and how learned execution of maneuvers can be performed without relying solely on localization and HD maps.

By:
Larry Jackel (NVIDIA, Deep Learning and Robotics Specialist)
October 18, 2017, 2:30 pm to 3:00 pm
Hall: Hall D Track: Auto Type: Talk
Accelerating Cyber Threat Detection with GPU
by Joshua Patterson (NVIDIA, Applied Solutions Engineering Director)
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Accelerating Cyber Threat Detection with GPU

Analyzing vast amounts of enterprise cyber security data to find threats can be cumbersome. Cyber threat detection is also a continuous task, and because of financial pressure, companies have to find optimized solutions for this volume of data. We'll discuss the evolution of big data architectures used for cyber defense and how GPUs are allowing enterprises to efficiently improve threat detection. We'll discuss (1) briefly the evolution of traditional platforms to lambda architectures and ultimately GPU-accelerated solutions; (2) current GPU-accelerated database, analysis tools, and visualization technologies (such as MapD, BlazingDB, H2O.ai, Anaconda and Graphistry), and discuss the problems they solve; (3) the need to move beyond traditional rule based indicators of compromise and use a combination of machine learning, graph analytics, and deep learning to improve threat detection; and finally (4) our future plans to continue to advance GPU accelerated cyber security R&D as well as the GPU Open Analytics Initiative.

By:
Joshua Patterson (NVIDIA, Applied Solutions Engineering Director)
October 18, 2017, 2:30 pm to 3:00 pm
Hall: Hall F Track: Cyber Security Type: Talk
Implementing Real-time Vision Solutions for Train Safety
by Shahar Hania (Rail Vision Israel, VP R&D, Co-Founder)
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Implementing Real-time Vision Solutions for Train Safety

Developing automated early-warning systems for train safety requires significant processing power and advanced vision algorithms. In this talk we will discuss the problems we are solving around using live video to analyze rail conditions and our experience developing on the NVIDIA platform. Get insight on CNN algorithm implementations for environments with constraints on compute power, peripherals, and interfaces. We will show examples running on NVIDIA GPUs and how to manage bottlenecks.

By:
Shahar Hania (Rail Vision Israel, VP R&D, Co-Founder)
October 18, 2017, 2:30 pm to 3:00 pm
Hall: Hall G Track: Smart Cities Type: Talk
Humanizing the Future of Mobility: Smart Transportation Language towards Acceptance of Autonomy (Presented by General Motors)
by Asaf Degani (General Motors, Technical Fellow, R&D)
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Humanizing the Future of Mobility: Smart Transportation Language towards Acceptance of Autonomy (Presented by General Motors)

Towards humanizing the autonomy – we need to first address the needs of the passenger "the human captive in the mobile robot" – the one who has given full faith in autonomy. Then we need to address the needs of the other humans – road users; ensure that autonomous transportation - a world where we humans are surrounded by a growing population of robots is a safer world; a world where traffic flows - we spend less frustrating commute time every day and experience prominent improvement to the quality of life. Once we gain the acceptance (realistically in the next ten to twenty-year time frame), we may envision adding new meanings to our lifestyle via self-driving vehicles – that can be ultimately our mobile home(office); actually our third address. In this talk, we will introduce you to a formal framework towards design(analysis) and evaluation of HRI language;and then we will go over the role of in-out cabin HRI data analytics and machine learning towards realization of this language.

By:
Asaf Degani (General Motors, Technical Fellow, R&D)
October 18, 2017, 2:30 pm to 3:00 pm
Hall: Hall H Track: Auto Type: Talk
Making Deep Object Detection Work in Medical Imaging
by Idan Bassuk (Aidoc Medical LTD, Chief Research Officer)
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Making Deep Object Detection Work in Medical Imaging

Our session will provide an overview of the medical image AI domain, including: Technological challenges unique to the medical image domain such as deep learning based on 3D images, high variability images, and the need to reach high accuracy results necessary for healthcare purposes. Leveraging high performance computing to provide solutions to the various challenges unique to medical image AI. We will share our latest insights relating to both the optimization of deep learning computing infrastructure and cutting edge types of deep learning architectures that we have tailor-made and implemented into our domain.

By:
Idan Bassuk (Aidoc Medical LTD, Chief Research Officer)
October 18, 2017, 2:30 pm to 3:00 pm
Hall: Hall I Track: Healthcare Type: Talk
3:00 pm Leveraging AI for Self-Driving Cars at GM
by Efrat Rosenman (General Motors Israel, Head of Cognitive Driving Group)
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Leveraging AI for Self-Driving Cars at GM

This talk will identify numerous challenges to make self-driving service a reality, including data annotation at scale. By applying GPU-based AI and ML to automatically annotate data accuracy can be increased. In addition, multi-modality-based perception can be utilized to improve confidence. Driving policy in complex environment is another intriguing challenge which motivates development in the field of reinforcement learning (RL). Specific aspects of RL will be discussed, such as the need to act in very high dimensional spaces and to make decisions with long-term consequences.

By:
Efrat Rosenman (General Motors Israel, Head of Cognitive Driving Group)
October 18, 2017, 3:00 pm to 3:30 pm
Hall: Hall D Track: Auto Type: Talk
  Integrating AI in Solutions for the Military Industries
by Yona Coscas (Elbit Systems, AI Team Leader)
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Integrating AI in Solutions for the Military Industries

Elbit Systems will discuss real time semantic segmentation using GPUs both on manned and unmanned aerial vehicles, and its applications in landing optimization. A large part of this work consists of designing a novel fully connected network that can handle 3D data as well as various 2D inputs (multi-spectral), 3D classification, developing methods for real time classification on point clouds using deep CNN. In the field of AI, they will discuss reinforcement and imitation learning as part of autonomous entities, anomaly detection on large scale databases (using DNNs), and time series and predictions on sensor data to identify loads on pilots.

By:
Yona Coscas (Elbit Systems, AI Team Leader)
October 18, 2017, 3:00 pm to 3:30 pm
Hall: Hall F Track: Cyber Security Type: Talk
Utilizing AI & GPUs to Build Cloud-based Real-Time Video Event Detection Solutions
by Zvika Ashani (Agent Video Intelligence, CTO & Co-Founder)
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Utilizing AI & GPUs to Build Cloud-based Real-Time Video Event Detection Solutions

In this session, you will learn about the challenges of creating a cloud-based video analytics service that can easily scale to process hundreds of thousands of cameras in real-time, by utilizing state-of-the-art AI running on GPUs. Most video analytics services in the cloud work in a "batch" format (offline), whereby a video clip is uploaded to the service, analyzed, and then results are delivered to the user. Performing video analytics in real-time on a large number of continuous video streams, and with low latency, poses a significant engineering challenge. Learn how Agent Video Intelligence has overcome these challenges to create innoVi video analytics service.

By:
Zvika Ashani (Agent Video Intelligence, CTO & Co-Founder)
October 18, 2017, 3:00 pm to 3:30 pm
Hall: Hall G Track: Smart Cities Type: Talk
Deep Learning for Helping in Diagnosis Tasks - A Radiologist Assistant Technology (Presented by IBM)
by Michal Rosen-Zvi (IBM Research, Haifa Research Labs, Israel, Director, Healthcare Informatics)
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Deep Learning for Helping in Diagnosis Tasks - A Radiologist Assistant Technology (Presented by IBM)

Deep Learning for Helping in Diagnosis Tasks - A Radiologist Assistant Technology (Presented by IBM)
This talk is about deriving insights and generating a Deep Learning based technology that is trained on Real World Data - electronic health records and medical images - and provides a support for radiologists and other physicians in their diagnosis process. Breast cancer diagnosis based on clinical history and medical imaging will serve as a running example while a set of technologies developed at IBM Research Haifa will be reviewed. The talk will also provide information regarding value gained from leveraging the IBM PowerAI platform - the world's fastest AI platform. It combines state of the art hardware with complete deep learning software distribution.

By:
Michal Rosen-Zvi (IBM Research, Haifa Research Labs, Israel, Director, Healthcare Informatics)
October 18, 2017, 3:00 pm to 3:30 pm
Hall: Hall H Track: Healthcare
Project Holodeck and NVIDIA Isaac
by Zvi Greenstein (NVIDIA, General Manager)
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Project Holodeck and NVIDIA Isaac

Project Holodeck is an intelligent, high-fidelity, virtual reality platform that empowers designers and inventors to bring their ideas to life. By sharing and exploring their creations in life-like virtual reality, designers will discover new ideas and streamline the review process. It allows designers to render detailed models photorealistically and at life-like scale as well as to simulate accurate physical interactions between people, objects and environment. Within Holodeck people will be able to collaborate naturally and in real-time in the same virtual environment wherever they are. Project Holodeck will also enhance workflows with NVIDIA Isaac, an AI-based software platform that lets developers train their virtual robot using detailed and highly realistic test scenarios. We'll discuss the use cases for Project Holodeck and Isaac. Holodeck's early access program will launch in the fall — come hear the talk, and then try Project Holodeck and Isaac demos at the NVIDIA booth!

By:
Zvi Greenstein (NVIDIA, General Manager)
October 18, 2017, 3:00 pm to 4:00 pm
Hall: Hall I Track: VR Type: Talk
Break
3:30 pm Deep Reinforcement Learning for Autonomous Highway Driving
by Ran El-Yaniv (Technion, Professor of Computer Science)
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Deep Reinforcement Learning for Autonomous Highway Driving

The recent success of deep reinforcement learning in playing Atari games and Go revitalized reinforcement learning (RL) and provided inspiration to tackle real-world control problems based on deep neural perception. Some of the challenges in real-world applications of RL are the large sample complexity of RL methods, the need to define effective reward functions, and the potential risk (to the agent or the environment) in early stages of the learning process and when applying exploration actions. In this talk we discuss a training scheme, consisting of supervised elements, aiming to help in applying RL on real-world tasks. The supervised elements are imitation learning, reward induction, and safety module construction. We implemented this scheme using deep convolutional networks and applied it to create an agent capable of autonomous highway steering over the well-known racing game Assetto Corsa.

By:
Ran El-Yaniv (Technion, Professor of Computer Science)
October 18, 2017, 3:30 pm to 4:00 pm
Hall: Hall D Track: Auto Type: Talk
Inception Awards
by NVIDIA
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Inception Awards

Witness the exciting NVIDIA Inception Awards where startups compete for the title of "Israel's Best AI Startup." This special event is open to all GTC Israel conference pass holders. For more information, visit https://www.nvidia.com/en-il/gtc/sessions/ai-startups/.

By:
NVIDIA
October 18, 2017, 3:30 pm to 5:30 pm
Hall: Hall E Track: General Deep Learning Type: Special Event
Evolution of the Rugged GPGPU Computer
by Dan Mor (Aitech Systems, GPGPU and HPEC Product Line Manager)
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Evolution of the Rugged GPGPU Computer

In recent years, Aitech Systems Ltd. has built many GPGPU computer systems and has deployed significant projects for the defense market. As the demand for accelerated computation is constantly growing, we needed to find a solution for SFF (small form factor) systems and proper heat dissipation. NVIDIA Jetson TX1/TX2 System on Module (SoM) offers a solution to these constraints by providing small form factor SoM, excellent CUDA performance and low power consumption. Dan will compare Jetson TX1/TX2 performance to existing GPGPU systems (based on NVIDIA MXM modules) and show how the defense industry can benefit from moving to the NVIDIA Jetson TX1/TX2.

By:
Dan Mor (Aitech Systems, GPGPU and HPEC Product Line Manager)
October 18, 2017, 3:30 pm to 4:00 pm
Hall: Hall F Track: Cyber Security Type: Talk
A Social Network of Intelligent Machines for Guest Entry and Security
by Lisa Dolev (Qylur, CEO)
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A Social Network of Intelligent Machines for Guest Entry and Security

How can we enable automated AI/ Deep learning based machines to evolve their specialties through colonies of "social networks of intelligent machines" (SNIM)? We will give an example of Qylur's QyNetTM machines cloud concept and how we utilize the power of SNIMs, GPU enabled deep learning and execution at edge systems, to enable a revolution in our guest entry operations and physical security for public venues. From mega events to parks and museums. We will also dream a bit further to how other industrial intelligent machines can benefit from the QyNet SNIM, and also touch on our responsibilities as humans as we enable this disruptive and beneficial revolution to take place.

By:
Lisa Dolev (Qylur, CEO)
October 18, 2017, 3:30 pm to 4:00 pm
Hall: Hall G Track: Smart Cities Type: Talk
Scale Your AI Solutions with NVIDIA GPUs on Azure (Presented by Microsoft)
by Yuval Mazor (Microsoft, Data Solutions Architect)
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Scale Your AI Solutions with NVIDIA GPUs on Azure (Presented by Microsoft)

As we all know, a single NVIDIA GPU is a powerhouse for building modern AI and Deep Learning applications. Now imagine what you can do with many! In this talk, we'll introduce you to the Microsoft Azure cloud platform, its capabilities and how – in a matter of minutes – you could have as many GPUs as you want doing your bidding!

By:
Yuval Mazor (Microsoft, Data Solutions Architect)
October 18, 2017, 3:30 pm to 4:00 pm
Hall: Hall H Track: General Deep Learning Type: Talk
Image Segmentation with TensorFlow**
by Jonas Lööf (NVIDIA, Solutions Architect)
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Image Segmentation with TensorFlow**

Image Segmentation with TensorFlow**
**This is a Deep Learning Institute hands-on training lab, which will require a "Conference & Training Pass." You will also need to bring your own laptop. To prepare for the lab, please follow instructions here: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/whitepages/DLI_Lab_Instructions.pdf

There are a variety of important applications that need to go beyond detecting individual objects within an image, and that instead need to segment the image into spatial regions of interest. An example of image segmentation involves medical imagery analysis, where it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells, so that you can isolate a particular organ. Another example includes self-driving cars, where segmenting an image into distinct areas is needed to understand road scenes. In this lab, you will learn how to train and evaluate an image segmentation network using TensorFlow.

By:
Jonas Lööf (NVIDIA, Solutions Architect)
October 18, 2017, 3:30 pm to 5:00 pm
Hall: Hall KLM Track: Deep Learning Institute Labs Type: Instructor Led Lab
4:00 pm Deep Learning Autonomous Driving Simulation
by Danny Atsmon (Cognata, CEO)
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Deep Learning Autonomous Driving Simulation

Realistic automotive simulation platforms, where virtual cars travel virtual roads in virtual cities in remarkably true-to-life conditions, will be a vital part of developing and testing autonomous vehicles. The technology behind the Cognata simulation engine is heavily based on Deep Learning, computer vision and other advanced AI methods. In this session we will present a cloud based simulation engine, how it works and how to develop with it.

By:
Danny Atsmon (Cognata, CEO)
October 18, 2017, 4:00 pm to 4:30 pm
Hall: Hall D Track: Auto Type: Talk
How to Run SQL Queries on TBs of Data - Using GPUs
by Jake Wheat (SQream Technologies, Lead Architect)
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How to Run SQL Queries on TBs of Data - Using GPUs

By using Thrust and CUB, SQream's lead architect Jake Wheat will demonstrate how to approach building a big data database using GPUs. Learn how SQream DB, powered by NVIDIA GPUs is able to handle hundreds of terabytes. We'll combine various techniques and optimizations during this talk. We will start by building a simple database engine, with basic support for a small amount of SQL features. After a few simple steps, we'll begin optimizing and enhancing the basic database until it can handle much larger data sets than the GPU can typically handle.

By:
Jake Wheat (SQream Technologies, Lead Architect)
October 18, 2017, 4:00 pm to 4:30 pm
Hall: Hall F Track: Cyber Security Type: Talk
Breakthroughs in Face Recognition Capability via Deep Learning and GPUs
by Neil Robertson (AnyVision, CTO)
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Breakthroughs in Face Recognition Capability via Deep Learning and GPUs

The new revolution of Artificial Intelligence is driven by Deep Learning technology, which is fundamentally reshaping what computers can do. This session will look into the development and acceleration of the world's leading face recognition technology, developed by AnyVision. Deep Learning is especially suitable for dealing with large training sets and has been recently applied in diverse domains such as computer vision, speech processing and natural language processing. AnyVision has adapted Deep Learning to face recognition and made major breakthroughs which are now being deployed on multiple sites internationally to secure borders and aid law enforcement.

By:
Neil Robertson (AnyVision, CTO)
October 18, 2017, 4:00 pm to 4:30 pm
Hall: Hall G Track: Smart Cities Type: Talk
Cisco AppDynamics for Developers (Presented by Cisco)
by Amit Granot (Cisco, Systems Engineer)
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Cisco AppDynamics for Developers (Presented by Cisco)

You are invited to learn how Cisco AppDynamics can create a culture of ongoing improvement and openness by making it simple for teams to get the data they want for their role in the DevOps toolchain.

By:
Amit Granot (Cisco, Systems Engineer)
October 18, 2017, 4:00 pm to 4:30 pm
Hall: Hall H Type: Talk
A Review of Semantic Segmentation with Deep Learning
by Eyal Gruss (Flatspace, Director of AI)
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A Review of Semantic Segmentation with Deep Learning

In this technical talk, Eyal will discuss the application of deep learning to semantic segmentation. He will discuss datasets, evaluation metrics, and losses, and review the architectures and training methods of the main papers of 2014-2017, including: FCN, DeepLab, DeconvNet, U-Net, SegNet, Dilated Convolutions, 100-Layer Tiramisu, Wide ResNet, PSPNet, Adversarial methods, PolygonRNN, Mask R-CNN and semi-supervised methods. Notice that this talks assumes familiarity with convnets.

By:
Eyal Gruss (Flatspace, Director of AI)
October 18, 2017, 4:00 pm to 4:30 pm
Hall: Hall I Track: General Deep Learning Type: Talk
4:30 pm Developing a National Infrastructure for Autonomous Systems
by Leon Altarac (Israel Defense Forces, Robotic Systems Knowledge Leader)
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Developing a National Infrastructure for Autonomous Systems

In order to adapt to the ever changing world, the Israel Defense Force together with the Ministry of Transportation are developing a modular laboratory to aid startups in testing their ideas and to certify them for use. Leon will discuss the process and architecture of building a modular autonomous vehicle and how developers / startups can access and use the platform. Discover and learn the vehicle's architecture, networking, modules, and APIs how to integrate with them and what you can to with them.

By:
Leon Altarac (Israel Defense Forces, Robotic Systems Knowledge Leader)
October 18, 2017, 4:30 pm to 5:00 pm
Hall: Hall D Track: Auto Type: Talk
NVIDIA ProViz Performance Engineering: Accelerating Workflows with Quadro vDWS & GRID
by Luke Wignall (NVIDIA, Sr Manager, ProViz Performance Engineering & Technical Marketing)
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NVIDIA ProViz Performance Engineering: Accelerating Workflows with Quadro vDWS & GRID

This session will cover the recent product announcements for GRID and vDWS. This includes CUDA/OpenCL across all vGPU profiles, added monitoring and management, and of course adding Tesla Pascal P6, P40, and P100's to the already exciting lineup of Maxwell M60, M10, and M6. Luke will cover the new use cases enabled by the addition of compute alongside visualization across all profiles, speak to this universal use of GPU across the data center, and how to build your own solutions with this powerful shared resource.

By:
Luke Wignall (NVIDIA, Sr Manager, ProViz Performance Engineering & Technical Marketing)
October 18, 2017, 4:30 pm to 5:00 pm
Hall: Hall F Type: Talk
Deep Learning for High Frequency Trading
by Yam Peleg (Deep Trading, Founder and Chairman)
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Deep Learning for High Frequency Trading

Deep learning have recently gained considerable attention in the speech transcription and image recognition for their superior predictive properties. Due to the chaotic behavior of financial markets one of the greatest challenges of todays financial researches is to accurately predict the future market prices of stocks, forex currencies and commodities. Yam will share some of his company trading experience using deep learning systems from the past couple of years and present a fully working algorithm for automated intra-day trading.

By:
Yam Peleg (Deep Trading, Founder and Chairman)
October 18, 2017, 4:30 pm to 5:00 pm
Hall: Hall G Track: General Deep Learning Type: Talk
AI Powered Video Analytics and Future of Intelligent Sports Sponsorship (Presented by SAP)
by Mike Kemelmakher (SAP, VP, Head of SAP Innovation Center Israel)
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AI Powered Video Analytics and Future of Intelligent Sports Sponsorship (Presented by SAP)

Global sponsorship budgets in 2016 reached US$60B, with media production companies generating hundreds thousands hours of sports related video content on an annual basis. Investments in sponsoring of sports events is now critical for many brands, so how can this spend be measured and optimized ? This session looks at where we are now with SAP Brand Impact solution: how we can accurately measure the exposure of brand visual assets seen on TV using Deep Learning and Computer Vision. We will share our recent experience with Volta GPUs and TensorRT3, and what the impact is on our solution performance.

By:
Mike Kemelmakher (SAP, VP, Head of SAP Innovation Center Israel)
October 18, 2017, 4:30 pm to 5:00 pm
Hall: Hall H Type: Talk
Mixed Precision Training of Deep Neural Network with Volta
by Boris Ginsburg (NVIDIA, Deep Learning Engineer)
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Mixed Precision Training of Deep Neural Network with Volta

We'll describe training of very deep networks with mixed-precision float ("float16") using Volta Tensor Core. Float16 has two major potential benefits: high training speed and reduced memory footprint. But float16 has smaller numerical range than regular single precision float, which can result in overflow or underflow ("vanishing gradient") during training. We'll describe simple rescaling mechanism which solves these potential issues. With this rescaling algorithm, we successfully used mixed precision training for such networks as Alexnet, GoogLeNet, Inception_v3, and Resnets without any loss in accuracy.

By:
Boris Ginsburg (NVIDIA, Deep Learning Engineer)
October 18, 2017, 4:30 pm to 5:30 pm
Hall: Hall I Track: General Deep Learning Type: Talk
5:00 pm Deep Learning in MATLAB for Automotive Applications: From Concept to Implementation
by Roy Fahn (Systematics - MathWorks, Application Engineer)
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Deep Learning in MATLAB for Automotive Applications: From Concept to Implementation

Learn how to design, develop, and deploy computer vision and deep learning automotive applications on to GPUs, whether on your desktop, a cluster, or on embedded Tegra platforms, including Jetson TK1/TX1/TX2 and DRIVE PX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB. Next, those networks are trained using MATLAB's GPU and parallel computing support either on the desktop, a local compute cluster, or in the cloud. Finally, a new compiler (released in September 2017) auto-generates portable and optimized CUDA code from the MATLAB algorithm, which is then cross-compiled and deployed to the Tegra board. We present benchmarks that show the superior performance of the auto-generated CUDA code (~7x faster than TensorFlow).

By:
Roy Fahn (Systematics - MathWorks, Application Engineer)
October 18, 2017, 5:00 pm to 5:30 pm
Hall: Hall D Track: Auto Type: Talk
Accelerate Analytics with a GPU Data Frame
by Aaron Williams (MapD, VP of Global Community)
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Accelerate Analytics with a GPU Data Frame

While rapid innovation is occurring across the GPU software ecosystem, the platforms themselves still remain isolated from each other - until now. Aaron Williams, the VP of Global Community at MapD, will demo the GPU Open Analytics Initiative's (GOAI) first project on stage - the GPU Data Frame (GDF) - and explain how this approach will enable efficient intra-GPU communication between different processes running on the GPUs.

By:
Aaron Williams (MapD, VP of Global Community)
October 18, 2017, 5:00 pm to 5:30 pm
Hall: Hall F Type: Talk
Practical Use Cases for Deep Learning and Machine Learning (Presented by HPE)
by Nir Oran (HPE, Chief Technologist)
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Practical Use Cases for Deep Learning and Machine Learning (Presented by HPE)

In the talk, we will discuss real world use cases for the above, with a focus on how to bring the technology "down to earth" and make the vision a reality.

By:
Nir Oran (HPE, Chief Technologist)
October 18, 2017, 5:00 pm to 5:30 pm
Hall: Hall G Type: Talk
Scaling Machine Learning – Enabling Enhanced GPU Performance for AI (Presented by Mellanox)
by Gil Bloch (Principal Architect, Mellanox Technologies), Gilad Shainer (Vice President, Marketing, Mellanox Technologies)
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Scaling Machine Learning – Enabling Enhanced GPU Performance for AI (Presented by Mellanox)

Come join us, and learn how to build a data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, scalable, low-latency network interconnects for both InfiniBand and Ethernet. We will briefly present the state of the art techniques for distributed machine learning, and what special requirements they impose on the system, followed by an overview of interconnect technologies used to scale and accelerate distributed machine learning including RDMA, NVIDIA's GPUDirect technology and in-network computing use to accelerates large scale deployments in HPC and artificial intelligence.

By:
Gil Bloch (Principal Architect, Mellanox Technologies), Gilad Shainer (Vice President, Marketing, Mellanox Technologies)
October 18, 2017, 5:00 pm to 5:30 pm
Hall: Hall H Track: Solutions Type: Talk
 
5:30 pm Happy Hour Networking Reception
by GTC Israel
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Happy Hour Networking Reception

Join us to connect with experts from NVIDIA and other great companies at the GTC Israel exhibit hall over great drinks and food.

By:
GTC Israel
October 18, 2017, 5:30 pm to 7:00 pm
Hall: Hall C Track: Solutions Type: Special Event
6:00 pm
6:30 pm

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