No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
My Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
There aren't any available sessions at this time.
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
Please enter a maximum of {0} characters.
{0} remaining of {1} character maximum.
Please enter a maximum of {0} words.
{0} remaining of {1} word maximum.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2019 GTC San Jose
Add to My Interests
Remove from My Interests
WORKSHOPS MARCH 17, 2019 | CONFERENCE MARCH 18-21, 2019


Login to your account and click “Add to Schedule” to build your personal agenda and reserve your place in Instructor-Led Training.

Schedule Grid View


Please note:
  • DLI Instructor-Led Workshops must be purchased separately through registration. See GTC Pricing for more information.
  • To attend Instructor-Led Training, you must have a Conference & Training pass and also reserve your place at each Instructor-Led Training.
  • If you have reserved a place at an Instructor-Led Training, please arrive on time to ensure you do not lose your seat.
  • Attendance to sessions is first come, first served. Please arrive early to the sessions you wish to attend to guarantee entry.
DLI Workshop Pass Required     |      Conference & Training Pass Required


Session Icon
DLIW901 - Instructor-Led Workshop: Fundamentals of Deep Learning for Computer Vision

Prerequisites: Familiarity with the basic programming fundamentals, such as functions and variables

Frameworks: Caffe, DIGITS

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

In this workshop, 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, training parameters, network structure, and other strategies to increase performance and capability
  • Deploy your neural networks to start solving real-world problems

Upon completion, you’ll be able to start solving problems on your own with deep learning.

 

Instructor-Led Workshop Alex Qi - Solutions Architect, NVIDIA
Add to My Interests
Session Icon
DLIW902 - Instructor-Led Workshop: Fundamentals of Deep Learning for Natural Language Processing

Prerequisites: Basic experience with neural networks and Python programming, familiarity with linguistics

Frameworks: TensorFlow, Keras

Certification: Available upon completion of code-based and multiple choice assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Learn the latest deep learning techniques to understand textual input using natural language processing (NLP). You’ll learn how to:

  • Convert text to machine-understandable representation and classical approaches
  • Implement distributed representations (embeddings) and understand their properties
  • Train machine translators from one language to another 

Upon completion, you’ll be proficient in NLP using embeddings in similar applications.

 

Instructor-Led Workshop Yuval Mazor - Senior Solutions Architect, NVIDIA
Add to My Interests
Session Icon
DLIW903 - Instructor-Led Workshop: Fundamentals of Deep Learning for Multi-GPUs

Prerequisites: Experience with stochastic gradient descent mechanics

Frameworks: TensorFlow

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU, or even years for the larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.
 
This workshop will teach you how to use multiple GPUs to training neural networks. You'll learn:

  • Approaches to multi-GPU training
  • Algorithmic and engineering challenges to large-scale training
  • Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks using TensorFlow.

 

Instructor-Led Workshop Adam Grzywaczewski - Deep Learning Solutions Architect, NVIDIA
Add to My Interests
Session Icon
DLIW904 - Instructor-Led Workshop: Deep Learning for Intelligent Video Analytics

Prerequisites: Experience with deep networks (specifically variations of CNNs), intermediate-level experience with C++ and Python

Frameworks: TensorFlow, TensorRT, Caffe

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

With the increase in traffic cameras, growing prospect of autonomous vehicles, and promising outlook of smart cities, there's a rise in demand for faster and more efficient object detection and tracking models. This involves identification, tracking, segmentation and prediction of different types of objects within video frames.

In this workshop, you’ll learn how to:

  • Efficiently process and prepare video feeds using hardware accelerated decoding methods
  • Train and evaluate deep learning models and leverage ""transfer learning"" techniques to elevate efficiency and accuracy of these models and mitigate data sparsity issues
  • Explore the strategies and trade-offs involved in developing high-quality neural network models to track moving objects in large-scale video datasets
  • Optimize and deploy video analytics inference engines by acquiring DeepStream SDK and TensorRT optimization tools

Upon completion, you'll be able to design, train, test and deploy building blocks of a hardware-accelerated traffic management system based on parking lot camera feeds.

 

Instructor-Led Workshop Kushan Ahmadian - Curriculum Developer, Deep Learning Institute, NVIDIA
Add to My Interests
Session Icon
DLIW905 - Instructor-Led Workshop: Deep Learning for Robotics

Prerequisites: Basic familiarity with deep neural networks, Basic coding experience in Python or a similar language

Frameworks: ROS, DIGITS, Jetson

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

AI is revolutionizing the acceleration and development of robotics across a broad range of industries. Explore how to create robotics solutions on a Jetson for embedded applications. You’ll learn how to: 

  • Apply computer vision models to perform detection 
  • Prune and optimize the model for embedded application 
  • Train a robot to actuate the correct output based on the visual input 

Upon completion, you’ll know how to deploy high-performance deep learning applications for robotics.

 

Instructor-Led Workshop Dana Sheahen - Curriculum Developer, Deep Learning Institute, NVIDIA
Add to My Interests
Session Icon
DLIW906 - Instructor-Led Workshop: Fundamentals of Accelerated Computing with CUDA Python

Prerequisites: Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. NumPy competency including the use of ndarrays and ufuncs. CUDA programming knowledge is not required.

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

This course explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to:

  • Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
  • Use Numba to create and launch custom CUDA kernels
  • Apply key GPU memory management techniques

Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.

 

Instructor-Led Workshop Robert Crovella - Solutions Architect, NVIDIA
Add to My Interests
Session Icon
SE0000 - Welcome Reception

At this reception, meet NVIDIA staff and other GTC alumni to get tips, especially if you're a first-timer.

Special Event
Add to My Interests
Session Icon
DLIT901 - Accelerating Applications with CUDA C/C++

Prerequisites: Basic experience with C/C++

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

The CUDA computing platform enables acceleration of CPU-only applications to run on the world's fastest massively parallel GPUs. Learn how to accelerate C/C++ applications by:

  • Exposing the parallelization of CPU-only applications, and refactoring them to run in parallel on GPUs
  • Successfully managing memory
  • Utilizing CUDA parallel thread hierarchy to further increase performance

Upon completion, you'll be able to utilize CUDA to accelerate your CPU-only C/C++ applications for massive performance gains.  

Instructor-Led Training Robert Crovella - Solutions Architect, NVIDIA
Add to My Interests
Session Icon
DLIT911 - Image Classification with DIGITS

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognize handwritten digits by:

  • Loading image data to a training environment
  • Choosing and training a network
  • Testing with new data and iterating to improve performance

Upon completion, you'll be able to assess what data you should be using for training.  

Instructor-Led Training David Williams - Solutions Architect, NVIDIA
Add to My Interests
Session Icon
DLIT913 - Image Segmentation with TensorFlow

Prerequisites:  Basic experience with neural networks

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. Learn how to segment MRI images to measure parts of the heart by:

  • Comparing image segmentation with other computer vision problems
  • Experimenting with TensorFlow tools such as TensorBoard and the TensorFlow Python API
  • Learning to implement effective metrics for assessing model performance

Upon completion, you'll be able to set up most computer vision workflows using deep learning.  

Instructor-Led Training Jonathan Bentz - Solutions Architect, NVIDIA
Add to My Interests
Session Icon
L9102 - Jetson Developer Tools Training Lab

Prerequisites: Basic CUDA-C and C++ coding skills. 

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Learn to maximize productivity when developing software for the Jetson platform. We'll explain how to manage source code on the host PC to cross-compile the software and initiate remote debugging sessions to debug CPU C/C++ and CUDA C code. We'll use a comprehensive set of exercises to teach you how to use NVIDIA's new suite of tools — Nsight Compute for optimizing CUDA kernels, Nsight Systems for optimizing CPU code and tracing multi-process system-wide activities, and Nsight Graphics for debugging and profiling 3D graphics applications.

 

Instructor-Led Training Daniel Horowitz - Director of Engineering in Developer Tools, NVIDIA
Add to My Interests
Session Icon
L9135 - Training: Dive Deep into GPU-Accelerated Investment Selection with Deep Learning

Prerequisites: None

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive. Learn how to use GPUs to accelerate the process of building a portfolio of securities that will perform well in the market. We'll describe our big data algorithm in Part 1, which uses a Sharpe Ratio on the full list of NYSE and NASDAQ candidate securities as a performance metric. We will outline a GPU kernel that we designed to measure the first two moments of daily log-returns and combine them. We then select top performers. In Part 2 we use deep learning in the form of a feed forward neural network with Keras and Tensorflow to train on income statements for securities in order to predict whether they will outperform the S&P 500 Index near term. In our lab we are able to examine each of these techniques and observe the GPU acceleration.

Instructor-Led Training Mark Bennett - Adjunct Lecturer, University of Iowa, Department of Management Sciences
Add to My Interests
Session Icon
S91009 - Modeling Stellar Explosions and Our Elemental Origins With Summit

After the Big Bang, the Universe contained hydrogen, helium, and a bit of lithium. Every other element on the periodic table is produced in stars and is disseminated into interstellar space via supernova explosions. Simulations of supernovae are among the most compute-intensive multi-physics applications on the world's largest modern supercomputers. We will discuss recent development of the FLASH code intended to make these simulations even more physically meaningful. In particular, we’ll describe how our work on FLASH, as part of the OLCF CAAR program, allowed us to increase the number of tracked nuclear species from about a dozen to hundreds, making precision predictions that can be compared to observations possible.

Talk Bronson Messer - Senior Scientist, ORNL
Add to My Interests
Session Icon
S91053 - IBM Developer Tutorial (Presented by IBM)

Exploring the Best Server for AI
Speaker: Samuel D. Matzek, Sr. Software Engineer
Speaker: Maria Ward, IBM Accelerated Server Offering Manager

Explore the server at the heart of the Summit and Sierra supercomputers, and the best server for AI. We will discuss the technical details that set this server apart and why it matters for your machine learning and deep learning workloads.

IBM Cloud for AI at Scale
Speaker: Alex Hudak, IBM Cloud Offering Manager

AI is fast changing the modern enterprise with new applications that are resource demanding, but provide new capabilities to drive insight from customer data. IBM Cloud is partnering with NVIDIA to provide a world class and customized cloud environment to meet the needs of these new applications. Learn about the wide range of NVIDIA GPU solutions inside the IBM Cloud virtual and bare metal server portfolio, and how customers are using them across Deep Learning, Analytics, HPC workloads, and more.

IBM Spectrum LSF Family Overview & GPU Support
Speaker: Larry Adams, Global Architect - Cross Sector, Developer, Consultant, IBM Systems

How to Fuel the Data Pipeline
Speaker: Kent Koeninger, IBM

IBM Storage Reference Architecture for AI with Autonomous Driving
Speaker: Kent Koeninger, IBM

 

Sponsored Talk Maria Ward - Program Director, Accelerated Systems Offering Management, IBM
Alex Hudak - Offering Manager, IBM
Larry Adams - Global Architect - Cross Sector, Developer, Consultant, IBM
Kent Koeninger - NA Unstructured Data/AI Specialist, IBM
Add to My Interests
Session Icon
S9144 - Human-Centered Autonomy This session will present a human-centered paradigm for building autonomous vehicle systems, contrasting it with the current industry approach. We will include discussion and video demonstration of new work at the Massachusetts Institute of Technology on driver-state sensing, voice-based transfer of control, annotation of large-scale naturalistic driving data, and the challenges of building and testing a human-centered autonomous vehicle. Talk Lex Fridman - Research Scientist, MIT
Add to My Interests
Session Icon
S9262 - Zero to GPU Hero with OpenACC Learn how to take an application from slow, serial execution to blazing fast GPU execution using OpenACC, a directives-based parallel programming language that works with C, C++, and Fortran. By the end of this session participants will know the basics of using OpenACC to write an accelerated application that runs on multicore CPUs and GPUs with minimal code changes. No prior GPU programming experience is required, but the ability to understand C, C++, or Fortran code is necessary. Tutorial Jeff Larkin - Senior DevTech Software Engineer, NVIDIA
Add to My Interests
Session Icon
S9276 - Toward Open-Domain Conversational AI Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines and final out more about the benchmark of models of prior work. We'll give an overview of dialogue research and details state-of-the-art end-to-end neural dialogue systems for both task-oriented and social chit-chat conversations. Tutorial Yun-Nung Chen - Assistant Professor, National Taiwan University
Add to My Interests
Session Icon
S9299 - Deploying NVIDIA vGPU with Red Hat Virtualization (RHV)

Red Hat Virtualization is an open platform that is built on Kernel-based Virtual Machine (KVM), one of several hypervisors supporting NVIDIA vGPU integration. Learn about RHV installation, the NVIDIA vGPU host driver, deployment of guest VMs with single and multiple vGPU enablement, as well as NVIDIA GRID license manager. 

Talk Sal Lopez - Solutions Architect, Red Hat
Shailesh Deshmukh - Sr. Solutions Architect, NVIDIA
Konstantin Cvetanov - Sr. Solution Architect, NVIDIA
Add to My Interests
Session Icon
S9346 - Sharing Physically Based Materials between Renderers with MDL We'll discuss the basics of NVIDIA's material definition language, showing how a single material can be used to define matching appearances between different renderers and rendering techniques. End users will learn how physically based definitions can be defined, while developers will learn what's entailed in supporting MDL within their own products or renderers. Talk Lutz Kettner - Director, Rendering Software and Material Definition, NVIDIA
Jan Jordan - Software Product Manager MDL, NVIDIA
Add to My Interests
Session Icon
S9347 - Performance Analysis for Large-Scale GPU-Accelerated Applications and DL Frameworks Get your hands on the latest versions of Score-P and Vampir to profile the execution behavior of your large-scale GPU-Accelerated applications. See how these HPC community tools pick up as other tools (such as NVVP) drop off when your application spans multiple compute nodes. Regardless of whether your application uses CUDA, OpenACC, OpenMP or OpenCL for acceleration, or whether it is written in C, C++, Fortran or Python, you will receive a high-resolution timeline view of all program activity alongside the standard profiles to identify hot spots and avenues for optimization. The novel Python support now also enables performance studies for optimizing the inner workings of deep learning frameworks. Tutorial Robert Henschel - Director, Science Community Tools, Indiana University
Guido Juckeland - Head of Computational Science Department, Helmholtz-Zentrum Dresden-Rossendorf
Add to My Interests
Session Icon
S9389 - Structural Sparsity: Speeding Up Training and Inference of Neural Networks by Linear Algorithms Learn how to achieve real-world speedup of neural networks using structural sparsity. Structural sparsity reduces the number of weights and computations in a way that's suitable for hardware acceleration. Over-parameterized neural networks waste memory and energy. Techniques like pruning or factorization can alleviate this during inference but they often increase training time, and achieving real-world speedups remains difficult. We'll explain how biology-inspired techniques can reduce the number of weights from quadratic to linear in the number of neurons. Compared to fully connected neural networks, these structurally sparse neural networks achieve large speedups during both training and inference, while maintaining or even improving model accuracy. We'll discuss hardware considerations and results for feed-forward and recurrent networks. Tutorial Matthijs Van Keirsbilck - Deep Learning Research Engineer, NVIDIA
Xiaodong Yang - Senior Research Scientist, NVIDIA
Alexander Keller - Director of Research, NVIDIA
Add to My Interests
Session Icon
S9397 - How to Create a Super Resolution Compositor that Scales to 32 Displays We'll discuss motivations for deploying multi-GPU solutions, which provide elegant solutions for a wide range of complex visualization solutions, from flight simulators to large display walls. Numerous methods are available for leveraging multi-GPU systems for real-time rendering and video playback. We'll detail the available methods and explain the advantages of each approach. Talk Thomas True - Senior Applied Engineer, Professional Video and Image Processing, NVIDIA
Add to My Interests
Session Icon
S9479 - Accelerating the Next Generation of Seismic Interpretation We will discuss how deep learning can automate complex seismic interpretation tasks that are crucial to exploration and production in the energy industry. Seismic interpretation often involves tasks of extracting structural features — horizons, faults, and salt bodies, for example — from 3D seismic images. Manually interpreting such seismic structural features can be time-consuming and labor-intensive. We'll explain how we're improving automatic seismic geobody interpretation by using a convolutional neural network for image classification and segmentation. Talk Yunzhi Shi - Graduate Research Assistant, The University of Texas at Austin
Add to My Interests
Session Icon
S9483 - Creating AI Work Groups Within the Enterprise: Developers Share Their Best Practices Learn from NVIDIA customers who will share their best practices for extending AI compute power to their teams without the need to build and manage a data center. These organizations will describe innovative approaches that let them turn an NVIDIA DGX Station into a powerful solution serving entire teams of developers from the convenience of an office environment. Learn how teams building powerful AI applications may not need to own servers or depend on data center access and find out how to take advantage of containers, orchestration, monitoring, and scheduling tools. The organizations will also show demos of how to set up an AI work group with ease and cover best practices for AI developer productivity. Talk Markus Weber - Senior Product Manager, NVIDIA
Michael Balint - Senior Product Manager, NVIDIA
Add to My Interests
Session Icon
S9501 - High Performance Distributed Deep Learning: A Beginner's Guide Learn about the current wave of advances in AI and HPC technologies to improve performance of DNN training on NVIDIA GPUs. We'll discuss exciting opportunities for HPC and AI researchers and give an overview of interesting trends in DL frameworks from an architectural/performance standpoint. Several modern DL frameworks offer ease of use and flexibility to describe, train, and deploy various types of DNN architectures. These typically use a single GPU to accelerate DNN training and inference. We're exploring approaches to parallelize training. We'll highlight challenges for message passing interface runtimes to efficiently support DNN training and discuss how efficient communication primitives in MVAPICH2 can support scalable DNN training. We'll also talk about how co-design of the OSU-Caffe framework and MVAPICH2 runtime enables scale-out of DNN training to 160 GPUs. Tutorial Dhabaleswar K (DK) Panda - Professor and University Distinguished Scholar, The Ohio State University
Hari Subramoni - Research Scientist, The Ohio State University
Ammar Ahmad Awan - Graduate Student, The Ohio State University
Add to My Interests
Session Icon
S9599 - Towards Weakly Supervised Scene Understanding with Deep Latent Variable Models and Structured Priors Our talk will cover the problem of weakly supervised learning for visual scene understanding. We'll explain the background for our research, the challenges involved, and why this research is important. We'll also touch on related work. Our talk will examine how we're addressing challenges and highlight two recent ECCV18 papers on edge alignment/unsupervised domain adaptation. We'll describe our ongoing work on weakly supervised object detection with image-level labels and look into some future directions for research. Talk Zhiding Yu - Research Scientist, NVIDIA
Add to My Interests
Session Icon
S9671 - AI Innovation Success Stories in Retail and Consumer Products Industries AI is driving success in many areas of the retail and consumer industries. Learn more about use cases, customer references, and compelling value propositions from GPU-Enabled technology. Topics include AI, computer vision, and machine learning. We'll discuss success stories with production-grade business impact that cultivate an innovative fast-fail approach to driving consumer engagement, brand awareness, and operational efficiency. Talk Scott Brubaker - Regional Manager, Retail & CPG, NVIDIA
Paul Hendricks - Solution Architect - Retail, NVIDIA
Add to My Interests
Session Icon
S9695 - Deep Learning and Beyond Learn how deep learning and GPU-Accelerated data science are delivering breakthrough results across a wide range of industries and application domains. We'll review the most effective neural networks for a variety of use cases, the latest GPU-Accelerated algorithms, and powerful application development tools and workflows. We'll also cover several best practices for managing data, effectively training models, and optimizing applications for high-performance production environments. Talk Will Ramey - Senior Director of Developer Programs and Deep Learning Institute, NVIDIA
Add to My Interests
Session Icon
S9713 - Quantized Neural Networks and QEngine We'll discuss network quantization — its background, methods, achievements, and the motivation behind it. Deep neural networks have achieved remarkable performance in a wide range of tasks. But DNNs are computationally intensive and resource-consuming, which hinders their use in embedded systems. We'll explain how we're working to alleviate this problem with quantized neural networks and a lightweight framework for efficient inference of these networks. Tutorial Yifan Zhang - Associate Professor, Institute of Automation, Chinese Academy of Sciences
Add to My Interests
Session Icon
S9733 - Role of Tensors in Machine Learning Learn about tensors, higher-order extensions of matrices that can incorporate multiple modalities and encode higher-order relationships in data. After an introduction to tensor methods, we will discuss which tensor methods can be used in deep learning and in probabilistic modeling. We'll show how tensor contractions, which are extensions of matrix products, provide high rates of compression in a variety of neural network models. We'll also demonstrate the use of tensors for document categorization at scale through probabilistic topic models. These are available in a python library called Tensorly that provides a high-level API for tensor methods and deep tensorized architectures. Tutorial Anima Anandkumar - Director of ML Research, NVIDIA
Add to My Interests
Session Icon
S9739 - Augmented Material Creation with Substance Alchemist and RTX Real-Time Inference

We'll discuss Substance Alchemist, a tool that will allow users to manage material collections and create new materials from pictures, scans, or pre-existing materials. We will detail the different facets of Alchemist and explain how GPU-Accelerated AI will enhance material creation, with a deep dive into the delighter features that leverage TensorCore. The tool was first demonstrated at SIGGRAPH 2018.

Talk Rosalie Martin - Senior Software Engineer, Allegorithmic
Baptiste Manteau - Substance Alchemist Product Owner, Allegorithmic
Add to My Interests
Session Icon
S9744 - Industrial AI: Probabilistic Physics-infused Deep Learning Applications We will highlight the power of hybrid probabilistic deep learning by discussing how this approach is used for building system-of-system models for large-scale systems such as refineries, power generation systems, and gas compression systems. We'll cover how GPUs accelerate all three applications, with a focus on a time series prediction model for predicting overall production in a large oil field with multiple changing parameters. Talk Mahadevan Balasubramaniam - Principal Data Scientist, BHGE - Digital
Arun Subramaniyan - Vice President Data Science & Analytics, BHGE - Digital
Add to My Interests
Session Icon
S9776 - Scaled Speech and Language Technology in the Contact Center We'll describe how large data scale (over two millennia of speech data per year) and low-latency requirements have enabled and required novel approaches to several speech and language models. Our talk will cover the GPU speech recognition training pipeline, continuous feedback-based training, optimizations for training, and inference on TensorRT for ultra- low latency text-to-speech models for call centers. We will discuss accuracy and latency benchmarks for speech recognition on conversational speech, speech synthesis, data-driven dialogue systems, emotion recognition, and speech act classification. We'll also demonstrate our system running on a scaled simulated call center and show live speech recognition, synthesis, and language processing. Talk Wonkyum Lee - Lead Speech Engineer, Gridspace
Anthony Scodary - Co-Founder, Gridspace
Alex Barron - Machine Learning Engineer, Gridspace
Add to My Interests
Session Icon
S9801 - RAPIDS: Deep Dive Into How the Platform Works RAPIDS is an open-source platform for GPU data science, incubated by NVIDIA. Built to look and feel like popular tools in the Python Data Science ecosystem, RAPIDS is easy to use and dramatically speeds up execution of all steps of a typical data science workflow. Intended for working data scientists, this session will be an in-depth walk through of all the stages of a model data science workflow using RAPIDS. The presentation will cover ingesting and cleaning data, feature engineering, working with strings, user-defined functions, and applying machine learning. The session will discuss the community and ecosystem around RAPIDS and future plans for the cuML library. Additionally, the session will cover how users can contribute to RAPIDS. At the end of the session, attendees will have learned RAPIDS benefits for data science, how to get started installing RAPIDS, and how to build their own workflows using RAPIDS. Tutorial Paul Mahler - Senior Data Scientist, NVIDIA
Add to My Interests
Session Icon
S9822 - Preparing Whole Slide Imaging Data for Deep Learning with Slideslicer Package

Learn how to prepare pathology whole-slide imaging for a machine learning experiment using open-source tools.

Tutorial Andrew Bishara - Postdoctoral Fellow in Deep Learning and Anesthesiology Clinical Instructor, University of California, San Francisco
Dmytro Lituiev - Postdoctoral Research Fellow, UCSF
Add to My Interests
Session Icon
S9935 - The Future of Virtual Reality: Visual Perception, Adaptive Rendering, and Robotics VR is rapidly evolving. HMD resolution and field of view are increasing, VR content is becoming more detailed, and demand for more realistic and more immersive experiences continues to grow. As we march forward in the pursuit of ever-better VR, how will we render fast enough to drive those higher resolution displays? How will we generate realistic content for enormous virtual worlds? How will we continue to enhance the quality and depth of immersion? In this panel, we'll cover topics such as human perception and neurophysiology, adaptive rendering strategies that focus compute power where it's needed, and deep learning-based synthesis for virtual models and environment. Learn how these components are being integrated to drive the future of VR. Panel Rachel Albert - Research Scientist, NVIDIA
Martina Sourada - Senior Director, SWQA Test/Tools Development, NVIDIA
Rochelle Pereira - Senior Software Engineering Manager, NVIDIA
Alisha Seam - Principal Engineer; Edge Computing Zone Lead, AT&T Foundry | Palo Alto
Lisa Bell-Cabrera - Director Business Development VR, NVIDIA
Claire Delaunay - Vice President of Engineering, NVIDIA
Add to My Interests
Session Icon
S9991 - Sensing Technologies for an Autonomous Tomorrow (Presented by Analog Devices)

We'll discuss how we're working to enable safe, reliable autonomous transport by developing highly accurate, real-time 3D views around autonomous vehicles. Our perception sensor suite uses RADAR, LIDAR, cameras, and IMUs to provide a 360-degree safety shield. We'll explain how data from high-performance imaging RADAR, LIDAR, and cameras are fused together to give the vehicle its sense of sight, while the IMU gives the vehicle its sense of feeling and ensures it maintains its heading. The large amount of data generated by our Drive360 sensors requires in-vehicle high performance AI computers to create a real-time 3D view around the vehicle.

Sponsored Talk Stewart Sellars - GM, LIDAR, Analog Devices
Add to My Interests
Session Icon
S91010 - Accelerating our Understanding of the Nuclear Physics and the Early Universe Understanding the emergence of nuclear physics from the underlying fundamental theory of strong interactions with Quantum chromodynamics (QCD) requires the fastest supercomputers. We will describe the role of QCD in the evolution of our universe and discuss how we use the latest supercomputers, such as Summit at Oak Ridge National Laboratory and Sierra at Lawrence Livermore National Laboratory, to address basic questions such as why does the universe contain more matter than antimatter? Looking towards the exascale era, we can dream of tackling more complex questions related to the rate of protons fusing to helium in the sun and the state of matter in extreme conditions such as neutron stars. We'll explain why making the most of these new computers will require clever software to take advantage of the heterogeneous architectures. We'll also describe some advances in optimized use of GPUs, as well as management of the complex set of tasks required. Talk André Walker-Loud - Staff Scientist, Lawrence Berkeley National Laboratory
Add to My Interests
Session Icon
S9177 - Integrating the NVIDIA Material Definition Language MDL in Your Application The NVIDIA MDL SDK provides a rich toolset to integrate MDL in a wide range of renderers, from physically based ray tracing to real-time applications. In this tutorial-like session, we'll show how MDL materials and texturing functions can be compiled for OptiX/CUDA, x86, and OpenGL target platforms. We'll also discuss how the MDL Distiller can be used to simplify MDL materials for use with real-time rendering solutions. Developers will learn about the available APIs and example code. Talk Lutz Kettner - Director, Rendering Software and Material Definition, NVIDIA
Add to My Interests
Session Icon
S9224 - Nucleus: Eight-GPU Platform For Visual Simulation We'll describe the Nucleus hardware platform, which combines eight NVIDIA GPUs in a single system to drive multi-channel displays. Nucleus is a new real-time 3D rendering platform that is able to handle demanding flight simulation applications, running Aechelon Technology's pC-NOVA image generation engine. Employing up to 8 Quadro GPUs in a 4U Windows 10-based server, a single system can handle the same OpenGL and CUDA workload as an 8-server cluster with minimal performance degradation. We present challenges encountered and overcome during Nucleus development, including Quadro-specific features and OpenGL extensions enabling the platform. We'll also discuss lessons learned and opportunities for future improvement. Talk David Morgan - Principal Engineer, Aechelon Technology
Add to My Interests
Session Icon
S9292 - Red Hat and the NVIDIA DGX: Tried, Tested, Trusted Red Hat and NVIDIA collaborated to bring together two of the technology industry's most popular products: Red Hat Enterprise Linux 7 and the NVIDIA DGX system. This talk will cover how the combination of RHELs rock-solid stability with the incredible DGX hardware can deliver tremendous value to enterprise data scientists. We will also show how to leverage NVIDIA GPU Cloud container images with Kubernetes and RHEL to reap maximum benefits from this incredible hardware. Talk Jeremy Eder - Senior Principal Software Engineer, Red Hat
Andre Beausoleil - Senior Principal Partner Manager, Red Hat, Inc.
Add to My Interests
Session Icon
S9314 - Beyond Supervised Driving The Toyota Research Institute is going beyond supervised learning for automated driving and exploring problems that affect research and development of long-term, large-scale autonomous robots. These problems include unsupervised domain adaptation, self-supervised learning, and robustness to edge cases. This session will dive into robotics systems, especially end-to-end vs. modular design and human-robot interaction. It will also include some of TRI's related research directions, especially those around world-scale cloud robotics. Talk Sudeep Pillai - Machine Learning Research Scientist, Toyota Research Institute
Adrien Gaidon - ML Lead, Toyota Research Institute (TRI)
Add to My Interests
Session Icon
S9370 - The Steady State: Reduce Spikiness from GPU uUtilization with MXNet We'll discuss monitoring and visualizing a deep neural network in MXNet and explain how to improve training performance. We'll also talk about coding best practices, data pre-processing, making effective use of CPUs, hybridization, efficient batch size, low precision training, and other tips and tricks that can improve training performance by orders of magnitude. Talk Cyrus Vahid - Principal Evangelist - AWS AI Labs, Amazon Web Services
Add to My Interests
Session Icon
S9404 - AI Vision for the Future of Retail

Artificial intelligence and the latest in computer vision techniques are quickly re-shaping the future of retail. By deploying modern deep learning techniques, technology companies are improving the overall retail shopping experience by getting rid of slow, cumbersome checkout lines. We'll talk about our work on autonomous checkout, which can make shopping a seamless, magical and more human interaction. Standard Cognition, along with other technology innovators like AmazonGo, have announced plans to deploy thousands of autonomous checkout-enabled retail stores by 2021.

Talk Jordan Fisher - CEO, Standard Cognition
Add to My Interests
Session Icon
S9424 - Video-Based Activity Forecasting for Construction Safety Monitoring Use Cases We'll talk about applying an LSTM-based trajectory forecasting framework to the problem of construction workers and equipment safety management, a problem with applications to activity forecasting, AEC industries, and AI smart cities. Our talk will provide an overview of construction safety management, construction site visual data collection and pre-processing, and forecasting model architecture. We'll discuss our rationale for designing a final model based on characteristics of construction data, and show experimental results as well as ablation study results. We'll also show a demo or video of our safety-management software. Talk Shuai Tang - Graduate Research Assistant, University of Illinois at Urbana-Champaign
Add to My Interests
Session Icon
S9455 - Deep Learning Framework for Diagnostics and Patient-Specific Design of Bioprosthetic Heart Valves

We'll present a deep learning-based analysis framework for making key decisions about heart valve replacement and valve design. We'll describe how we use deep learning to predict valve performance measures, which makes these measurements accessible to physicians who lack expert computational knowledge. We will explain how our trained DL framework can be used interactively to predict valve-performance measures with the same fidelity as time-consuming biomechanics simulations. We'll also discuss how our tool can help doctors with heart valve diagnosis, ultimately improving patient care.

Talk Adarsh Krishnamurthy - Assistant Professor, Iowa State University
Aditya Balu - Graduate Research Assistant, Iowa State University
Add to My Interests
Session Icon
S9575 - Unsupervised Learning of Depth, Odometry, Flow and Segmentation using Competitive Collaboration Learn about Competitive Collaboration, a framework that facilitates joint learning among several neural networks by introducing competition and collaboration. Competitive Collaboration is a three-player game in which two adversaries compete for a resource that is regulated by a moderator, where the moderator trains by a consensus between the adversaries. We'll describe how we apply our framework for joint unsupervised learning to four problems in computer vision — single-image depth prediction, camera motion estimation, optical flow, and motion segmentation. These problems are coupled by geometry of the world and so geometric constraints are exploited to facilitate learning without the need for labels. We will show that joint learning using our framework achieves state-of-the art results on all the subproblems among unsupervised methods. Talk Jonas Wulff - Postdoctoral Researcher, MIT
Anurag Ranjan - PhD Student, Max Planck Institute for Intelligent Systems
Add to My Interests
Session Icon
S9717 - Real-Time Ray Tracing on Professional Head-Mounted Displays with NVIDIA RTX We'll discuss recently available GPU hardware support for ray tracing, and examine how this makes it possible to fully ray-trace scenes in real time, even on high-end head-mounted displays (HMDs). Real-time ray tracing has been considered the "Holy Grail" of rendering, but existing solutions like NVIDIA OptiX were limited to moderate display resolutions and frame rates. As a result, they weren't feasible for ray tracing on head-mounted displays (HMDs), which typically feature a wide field of view and require constant high frame rates to avoid motion sickness. We'll examine how GPU hardware support from technologies like NVIDIA's Turing and RTX allows users to fully ray trace scenes on HMDs. Talk Jan Wurster - Solution and Technology Expert, ESI
Andreas Dietrich - Senior Software Developer Visualization, ESI Group
Add to My Interests
Session Icon
S9749 - AI Deployment in Manufacturing: Deep Learning Visual Inspection to Improve Productivity Manufacturers are increasingly adopting AI to improve productivity. We'll discuss our work to automate the inspection process, which represent 20 percent of the manufacturing pipeline. We're developing deep learning for automated visual inspection, aiming for human-level accuracy using NVIDIA GPUs and TensorRT to deploy the neural network on Jetson AGX Xavier. We'll also introduce our other new deep learning products. Talk Keisuke Fujita - AI Project Co-Founder, Musashi Seimitsu Industries
Shingo Fukui - AI Engineer, Musashi Seimitsu Industry
Add to My Interests
Session Icon
S9887 - Modernize Digital Workspace to Meet the Modern Workforce Demand Modern workers expect a satisfying experience over any network from any device. IT admins want the flexibility to deliver workloads from their hypervisor or cloud of choice. And modern applications like Windows 10 are more graphic-intensive than ever. Learn how Citrix and NVIDIA provide a superior a customer experience for graphic-accelerated virtual apps and desktops with true hypervisor and cloud flexibility. We'll discuss our latest innovations in graphics virtualization and describe other Citrix HDX innovations that enhance graphics remoting and optimize user experience. Talk Jared Cowart - Product Manager, NVIDIA
James Hsu - Technical Alliance Director, Citrix
Add to My Interests
Remove From Schedule Add To Schedule Are you sure you would like to Delete this personal time? Edit My Schedule Edit Personal Time This session is full. Would you like to be added to the waiting list? Would you like to remove "{0}" from your schedule? Would you like to add "{0}" to your schedule? Sorry, this session is full. Waitlist Available Sorry, this session and it's waiting list are completely full. Sessions Available Adding this multi-day session automatically enrolls you for all times shown below. Removing this multi-day session automatically removes you for all times shown below. Adding this multi-day session automatically enrolls you for all session times for this session. Removing this multi-day session automatically removes you for all session times for this session. Click to view details Interests Hide Interests Search Sessions Export Schedule There is a scheduling conflict. You cannot add this session to your schedule because you are participating in another session at this time. Schedule Conflict. An error occurred while processing this request.. Adding this item creates a conflict with another session on your schedule. Remove from Waiting List Add to Standby List Removing this will remove you from the waiting list for all session times for this session Adding this will add you to the waiting list for all session times for this session. You have nothing scheduled Tap below to see a list of sessions and activities that are available to add to your schedule this week Choose from the list of sessions to the left to add to your schedule for the day Add a Session

Registration Complete!

So we can prepare the best experience for you,
What can we do to help you?
Click here to skip
All Tab
The All tab
Attendee Tab
The Attendee Tab
Tailored Experiences
The Tailored Experiences Tab
Session Tab
The Session Tab
Speaker Tab
The Speaker Tab
Exhibitor Tab
The Exhibitor Tab
Files Tab
The Files Tab
Search Box
The search box
Filters
Filters
Dashboard
Dashboard Link
My Schedule
My Schedule Link
Recommendations
Recommendations Link
Interests
Interests Link
Meetings
Meetings Link
Agenda
Agenda Link
My Account
My Account Link
Catalog tips
Show More Sessions