THURSDAY - OCTOBER 18, 2018
 
04:00 PM
04:35 PM

Deep Learning Infrastructure for Autonomous Vehicles

We'll introduce deep learning infrastructure for building and maintaining autonomous vehicles. This includes techniques for managing the lifecycle of deep learning models from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop...


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Presenter:
Adam Henryk Grzywaczweski, Solution Architect, NVIDIA
Room: Hall B

SIL8114 - Deep Learning and AI, Autonomous Vehicle Technologies, AI in Healthcare
01:00 PM
01:45 PM

How GPU Compute has Enabled Next Level Functionality and Robustness for Driver Monitoring

As autonomous vehicles make progress, it is important for cameras to better "understand" the driver, occupants, and the entire cabin. With the progress of deep learning, it is now possible to provide extremely accurate driver and cabin monitoring, in real time, using inexpensive camera sensors and available GPU ....


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Presenter:
Ophir Herbst, CEO, Jungo Connectivity Ltd.
Room: Hall B

SIL8107 - Autonomous Vehicle Technologies
03:00 PM
03:45 PM

Pushing the Boundaries - Simulation vs. Real Life

The greatest challenge regarding simulation is finding the matrix that compares it to real life. In this session, Danny will present a mathematical matrix that defines this relation and show how a proper simulation can be constructed based on deep learning techniques. He will also supply live examples of the Cognata simulation platform.


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Presenter:
Danny Atsmon, CEO, Cognata Ltd.
Room: Hall B

SIL8122 - Deep Learning and AI, Autonomous Vehicle Technologies, VR and Simulation
05:00 PM
05:45 PM

Reinforcement Learning with A* and a Deep Heuristic

Inspired by the recent achievements of methods which combine trees and DNNs (e.g. AlphaZero), this study demonstrates how to effectively combine AI with a deep heuristic represented by a DNN. AI is a highly popular and successful path-planning algorithm, but it's usability is limited to only those domains where a good heuristic is known. This new algorithm, which we call Aleph-0, enables us to replicate the success of AI in new domains, including that of autonomous vehicles.


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Presenter:
Ariel Keselman, Chief Architect, Imagry
Room: Hall B

SIL8124 - Deep Learning and AI, Autonomous Vehicle Technologies
02:00 PM
02:45 PM

The Missing Sense - Enabling Autonomous Vehicles to "feel" the Road using Tactile AI

Like people, Autonomous Vehicles (Level 2 through 5) need to "see" and "feel" the road to best perform (in an enjoyable, efficient and safe way). To date, this "feel" aspect has been under-served. In this session, we will list novel methods for applying Artificial Intelligence to vehicle sensors in order to provide vehicles with advanced tactile sensing capabilities and augment the vastly-used visual sensors. ...


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Presenter:
Boaz Mizrachi, Founder and CTO, Tactile Mobility
Room: Hall B

SIL8113 - Deep Learning and AI, Autonomous Vehicle Technologies
01:00 PM
01:45 PM

Deep Learning and Beyond

Attend this session to learn how Deep Learning is delivering breakthrough results across a wide range of industries and applications. We'll review the most effective neural networks for a variety of use cases, the latest GPU-accelerated algorithms...


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Presenter:
Will Ramey, Sr. Director, Deep Learning Institute, NVIDIA
Room: Hall K/L

SIL8142 - Deep Learning and AI, Autonomous Vehicle Technologies, Accelerated Data Science, Professional Visualization, AI in Healthcare
02:00 PM
02:45 PM

Deep Learing for Image Understanding

In this session, Amir will describe deep learning models that can use context to make predictions. Specifically, he will describe recent models for image annotation and text categorization. The models we consider iteratively refine their prediction by considering how each prediction components is influenced by other components...


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Presenter:
Amir Globerson, Associate Professor of Computer Science, Tel Aviv University
Room: Hall K/L

SIL8135 - Deep Learning and AI, Autonomous Vehicle Technologies, Accelerated Data Science, Professional Visualization
09:00 AM
04:15 PM

Fundamentals of Deep Learning with Computer Vision

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities. In this course, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to: implement common deep learning workflows, such as image classification and object detection, experiment with data...


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Presenter:
Adam Henryk Grzywaczweski, Solution Architect, NVIDIA
Room: Halls C1&C2
- Autonomous Vehicle Technologies
01:00 PM
03:00 PM

Introduction to Object Detection with TensorFlow

This workshop is a lightning introduction to object detection and image segmentation for data scientists, engineers, and technical professionals. This task of computer-based image understanding permeates many major fields such as advertising, smart cities, healthcare, national defense, robotics, and autonomous driving. .....


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Presenters:
Oliver Knieps, Deep Learning Software Engineer, NVIDIA
Jan Jamaszyk, Software DevTech Engineer, NVIDIA

Room: Hall C2
- Autonomous Vehicle Technologies
03:30 PM
05:30 PM

Training Semantic Segmentation for DRIVE PX

The level of accuracy needed for urban driving is different than highway driving due to the density of different objects in a given scene. Using CamVid dataset, this mini course will go through all the steps required to do semantic segmentation given the computation capabilities of DRIVE PX. You’ll learn how to:....


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Presenters:
Oliver Knieps, Deep Learning Software Engineer, NVIDIA
Jan Jamaszyk, Software DevTech Engineer, NVIDIA

Room: Halls C4&C5
- Autonomous Vehicle Technologies
02:00 PM
02:25 PM

Acelerating Research to Production with PoTorch 1.0 and ONX (Presented by Facebok)

Facebok's strength in AI inovation comes from its ability to quickly bring cuting-edge research into large scale production using a multi-faceted tolset. Learn how...


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Presenter:
Sarah Bird, Technical Program Manager, Facebook
Room: Hall H

SIL8141 - Autonomous Vehicle Technologies
01:00 PM
01:25 PM

DDN A3I Storage Solutions: Extract More Answers From Your Data at Any Scale (Presented by DDN Storage)

DDN is a leader in storage solution for data intensive workflows at scale. This session will cover DDN's activity across the various markets of data intensive AI and Machine learning. The session is built from market coverage down to the underlaying technology that DDN provides and its advantages for NVIDIA DGX and GPU accelerated workflows. ...


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Presenter:
Ran Pergamin, Senior Storage Solution Architect, DDN Storage
Room: Hall H

SIL8150 - Deep Learning and AI, Autonomous Vehicle Technologies, Accelerated Data Science, AI in Healthcare
04:00 PM
04:25 PM

Real-time Edge Computing Deployment on Railway Applications (Presented by RailVision)

The train world is going through a massive change in order to increase safety and efficiency. One of the ways to do that is by increasing digitalization and implementing more intelligent systems for safety, maintenance and efficient operation to pave the way for autonomous train operating. RailVision company is a pioneer of implementing AI/DL technologies into this market and already involved in revolutionary projects deploying this technology. In this talk we will show a safety-related train application with maintenance capabilities that uses edge computing. Real-time considerations will be presented as a part of an overall view of deep learning implementation in a system. The connection between the training phase through optimization to the inference will also be discussed. Demos of edge computing capabilities will be presented.


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Presenter:
Shahar Hania, VP R&D, Co-Founder, RailVision
Room: Hall H

SIL8151 - Deep Learning and AI, Autonomous Vehicle Technologies