165 sessions
  • High Performance Computing
    Talk
    Accelerated Data Science
    AI Application/Deployment/Inference
    Government / National Labs
    Intermediate Technical
    3:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll discuss the potential to accelerate the numerical simulation of air quality using machine learning. Air pollution is a major environmental risk factor and one of the common causes of premature death globally. However, addressing the issue is difficult due to the cost of numerical air quality models, which are computationally expensive due to the complexity of the chemical processes responsible for air pollution’s formation. We’ll discuss our use of Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform to generate gradient-boosted tree models that can simulate the formation of air pollution with decent accuracy. We’ll explain how recent advances in Dask-XGBoost enabled us to increase training size, and how this is necessary for improving model skill. The boosted tree models, trained on NASA Center for Climate Simulation’s Advanced Data Analytics Platform, can be coupled with a full earth system model such as NASA’s Goddard Earth Observing System Model. This will provide a detailed global simulation of air quality at a much lower computational cost.

    , Research Scientist, NASA GMAO / USRA

    Primary Topic: High Performance Computing
    Submission Type: Talk
    Other Topics: Accelerated Data Science, AI Application/Deployment/Inference
    Industry Segments: Government / National Labs
    Audience Level: Intermediate Technical
    • Wednesday, Nov 63:30 PM - 3:55 PM EST
      Horizon
  • AI Application/Deployment/Inference
    Sponsored Talk
    General
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91499-accelerating-ai-and-machine-learning-with-containers-and-the-kubernetes-platform-from-red-hat/">View VIDEO</a>
    2:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll provide an overview of containers and Kubernetes, and how these technologies can help data scientists, machine learning engineers, and application developers solve challenges using trusted Red Hat platforms. We’ll share ways in which data scientists can accelerate their machine learning initiatives with Red Hat Enterprise Linux, containers, and the Red Hat OpenShift Kubernetes platform.

    , Red Hat

    , Red Hat

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Sponsored Talk
    Industry Segments: General
    Audience Level: Beginner Technical
    • Wednesday, Nov 62:00 PM - 2:25 PM EST
      Oceanic
  • Accelerated Data Science
    Instructor-Led Training
    Developer Tools
    General
    Beginner Technical
    Tuesday, Nov 05
    2:00 p.m. Tuesday, Nov 05

    Prerequisites: Experience as a data scientist or data analyst with professional competency with Pandas, NumPy, and scikit-learn

    The open-source RAPIDS project allows data scientists to GPU-accelerate their data science and data analytics applications from beginning to end, creating possibilities for drastic performance gains and techniques not available through traditional CPU-only workflows. Learn how to GPU-accelerate your data science applications by:


    · Utilizing key RAPIDS libraries like cuDF (GPU-enabled Pandas-like dataframes) and cuML (GPU-accelerated machine learning algorithms)
    · Learning techniques and approaches to end-to-end data science, made possible by rapid iteration cycles created by GPU acceleration
    · Understanding key differences between CPU-driven and GPU-driven data science, including API specifics and best practices for refactoring

    Upon completion, you'll be able to refactor existing CPU-only data science workloads to run much faster on GPUs and write accelerated data science workflows from scratch.

    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 courses.nvidia.com/join before you arrive.

    , Solutions Architect, NVIDIA

    Primary Topic: Accelerated Data Science
    Submission Type: Instructor-Led Training
    Other Topics: Developer Tools
    Industry Segments: General
    Audience Level: Beginner Technical
    • Tuesday, Nov 52:30 PM - 4:15 PM EST
      UMD at Ronald Reagan Bldg, Smith School of Business Classroom 2
  • AI & Deep Learning Research
    Talk
    High Performance Computing
    General
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91167-accelerating-deep-learning-with-nvidia-gpus-and-mellanox-interconnect/">View VIDEO</a>
    10:00 a.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll demonstrate how to build a scalable, high-performance, data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, low-latency network interconnects for both InfiniBand and Ethernet. We’ll present state-of-the-art techniques for distributed machine learning, and explain what special requirements they impose on the system. There will be an overview of interconnect technologies used to scale and accelerate distributed machine learning. This will include RDMA, NVIDIA’S GPUDIRECT technology and in-network computing platform, which is used to accelerate large-scale deployments in HPC and artificial intelligence.

    , Principal Architect, Mellanox

    Primary Topic: AI & Deep Learning Research
    Submission Type: Talk
    Other Topics: High Performance Computing
    Industry Segments: General
    Audience Level: Beginner Technical
    • Wednesday, Nov 610:00 AM - 10:50 AM EST
      Atrium Hall
  • Healthcare and Life Sciences
    Talk
    AI & Deep Learning Research
    Healthcare & Life Sciences
    Intermediate Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91274-accelerating-genomics-with-deep-learning/">View VIDEO</a>
    1:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll discuss the application of deep learning to genomic datasets, a rapidly-growing practice primed to revolutionize genomic analysis. The NVIDIA genomics team is building deep learning tools that are improving, accelerating, and reducing the cost of genomic analysis across sequencing instruments and applications. We’ll present two examples of our research, the first being a deep neural network for variant calling, which matches the performance of state-of-the-art variant calling tools. The second is a deep learning model that denoises low-quality ATAC-Seq data, reducing the cost of ATAC-Seq and enabling high-quality sequencing results from small numbers of cells.

    , Deep Learning Genomics Scientist, NVIDIA

    Primary Topic: Healthcare and Life Sciences
    Submission Type: Talk
    Other Topics: AI & Deep Learning Research
    Industry Segments: Healthcare & Life Sciences
    Audience Level: Intermediate Technical
    • Tuesday, Nov 51:30 PM - 2:20 PM EST
      Atrium Ballroom A
  • AI Application/Deployment/Inference
    Talk
    AI & Deep Learning Research
    General
    Beginner Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91177-accelerating-speech-to-text-using-kaldi/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91177-accelerating-speech-to-text-using-kaldi.pdf">View PDF</a>
    10:00 a.m. Wednesday, Nov 06
    Wednesday, Nov 06
    NVIDIA and Intelligent Voice have been accelerating the Kaldi speech recognition framework using CUDA. This has been a continued effort and progress has been received with interest at previous GTC presentations. In these presentations amazing single GPU performance was demonstrated by showing LibriSpeech processing at 3500x real-time but multi-GPU scalability was limited. Recent developments include additional performance gains and the acceleration of feature extraction which greatly improved multi-GPU performance leading to near linear scalability on DGX-2. In addition to presenting these performance gains NVIDIA, Intelligent Voice, and HPE have partnered to bring speech recognition to the mass markets. For real world and multi-language applications, Intelligent Voice has more than 20 models that are now fully accelerated in CUDA. To better serve the speech market NVIDIA, Intelligent Voice, and HPE utilising their partnership to offer a pre-configured speech processing solution, details of which will be shared at this talk. While Kaldi is just a framework, IV provides an enterprise-strength, fully scalable speech platform to meet the most demanding applications in government, financial services and other commercial applications.

    , CTO, Intelligent Voice

    , NVIDIA

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Talk
    Other Topics: AI & Deep Learning Research
    Industry Segments: General
    Audience Level: Beginner Technical
    • Wednesday, Nov 610:00 AM - 10:50 AM EST
      Hemisphere A
  • Accelerated Data Science
    Sponsored Talk
    Government / National Labs
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91474-accelerating-trash-detection-for-the-sf-estuary-institute-with-gpu-enabled-convolutional-neural-networks-powered-by-kinetica/">View VIDEO</a>
    5:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll explore how the GPU-accelerated Kinetica Active Analytics Platform enables data scientists to accelerate and operationalize machine learning at scale in a real-world use case involving trash detection in urban waterways.

    , Program Director for Environmental Informatics, Kinetica

    , Cofounder, CTO, Kinetica

    Primary Topic: Accelerated Data Science
    Submission Type: Sponsored Talk
    Industry Segments: Government / National Labs
    Audience Level: Beginner Technical
    • Tuesday, Nov 55:30 PM - 6:20 PM EST
      Oceanic
  • AI Policy
    Panel
    Government / National Labs
    Business/Executive Level
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91381-ai-and-cybersecurity-opportunities-and-threats-to-businesses-government-and-individuals/">View VIDEO</a>
    4:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll discuss the threat that massive data breaches and cyberattacks present to online services. AI is making both offensive and defensive tools more powerful. Our panel will explore the new capabilities made possible with AI, potential countermeasures, and the preparations being made by the administration and Congress.

    , Senior Director, U.S. Policy, Palo Alto Networks

    , VP, Intellectual Property and Cybersecurity, NVIDIA

    , Subcommittee Director - Cybersecurity, Infrastructure Protection, Committee on Homeland Security

    , Director, AI Infrastructure, NVIDIA

    , Associate Director, National Risk Management Center, Cybersecurity and Infrastructure Security Agency (CISA)

    Primary Topic: AI Policy
    Submission Type: Panel
    Industry Segments: Government / National Labs
    Audience Level: Business/Executive Level
    • Tuesday, Nov 54:30 PM - 5:20 PM EST
      Atrium Hall
  • Networking Event
    General
    Aerospace
    Defense
    Government / National Labs
    Higher Education / Research
    Retail / Etail
    7:00 a.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll host a breakfast roundtable to address the challenges of collecting massive amounts of data in order to develop new AI capabilities. To be useful, the data must be efficiently accessible in high-quality collections. Our panelists will discuss the hurdles their programs have faced, and present best practices for data storage, ingestion, labeling, and location selection for model training. We’ll address the implications of data quality and integrity, and advise on how to prepare for increasing demand on data stores.

    , Senior Solutions Architect, NVIDIA

    , Vice President, Technology, Slingshot Aerospace

    , Principal AI Engineer, JHU Applied Physics Laboratory

    , Chief Data Officer, United States Special Operations Command

    , VP & CDAO (Chief Data & Analytics Officer), Lockheed Martin

    Submission Type: Networking Event
    Industry Segments: General, Aerospace, Defense, Government / National Labs, Higher Education / Research, Retail / Etail
    • Wednesday, Nov 67:30 AM - 9:15 AM EST
      Atrium Ballroom A
  • AI Application/Deployment/Inference
    Panel
    Accelerated Data Science
    AI & Deep Learning Research
    General
    Intermediate Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91204-ai-implementers-panel/">View VIDEO</a>
    4:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll bring together AI implementers who have deployed deep learning at scale using NVIDIA DGX Systems. There will be a focus on specific technical challenges, solution design considerations, and the best practices that implementers learned from their respective solutions. Attendees will learn how to set up their AI projects for success by matching the right hardware and software platform options to their use cases and operational needs. They’ll also master how to design architecture to overcome unnecessary bottlenecks inhibiting scalable training performance, and how to build end-to-end AI development workflows that enable productive experimentation, training at scale, and model refinement.

    , Computer Engineer, U.S. Army Research Laboratory

    , VP, Engineering, Paige

    , Director of Analytics, Maxar

    , Director, AI Systems and Data Science Platform, NVIDIA

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Panel
    Other Topics: Accelerated Data Science, AI & Deep Learning Research
    Industry Segments: General
    Audience Level: Intermediate Technical
    • Tuesday, Nov 54:30 PM - 5:20 PM EST
      Horizon
  • Intelligent Machines, IoT & Robotics
    Networking Event
    General
    Aerospace
    Defense
    Government / National Labs
    Manufacturing
    Retail / Etail
    Other
    Tuesday, Nov 05
    7:00 a.m. Tuesday, Nov 05
    We’ll host an exclusive, invitation-only event to share lessons and best practices for applying AI to predictive maintenance, and present sustainment use cases for mission critical assets, systems, and processes. Topics will include industrial inspection, predictive maintenance, logistics, and conversational AI. Attendees will share with and learn from NVIDIA and their peers to continue developing the fundamental technologies that promote safe, reliable, and efficient operations.

    , President, PHM Society

    , Senior Manager - Analytics, Lockheed Martin

    , Research Aerospace Engineer, U.S. Army Research Laboratory

    , VP of Defense and Intelligence, SparkCognition

    , Director of Strategic Engagement, Defense Innovation Unit (DIU)

    Primary Topic: Intelligent Machines, IoT & Robotics
    Submission Type: Networking Event
    Industry Segments: General, Aerospace, Defense, Government / National Labs, Manufacturing, Retail / Etail, Other
    • Tuesday, Nov 57:30 AM - 9:15 AM EST
      Atrium Ballroom A
  • AI Application/Deployment/Inference
    Panel
    Government / National Labs
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91515-artificial-intelligence-to-improve-citizen-services/">View VIDEO</a>
    11:00 a.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll show how AI is driving the growth of the American economy, enhancing economic and national security, and improving quality of life. We’ll bring together Suzette Kent, CIO of the U.S. federal government, and a panel of civilian agency leaders to discuss how AI is improving citizen services. This will include data, training, use cases, and examples of partnership between the public and private sectors. We’ll also host a Q&A.

    , Federal Chief Information Officer, U.S. Office of Management and Budget

    , Chief Executive Officer, Government Executive Media Group

    , Chief Information Officer, National Science Foundation (NSF)

    , Assistant Secretary of Commerce for Environmental Observation and Prediction, performing the duties of Under Secretary of Commerce for Oceans and Atmosphere, NOAA

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Panel
    Industry Segments: Government / National Labs
    Audience Level: Beginner Technical
    • Tuesday, Nov 511:00 AM - 11:50 AM EST
      Atrium Hall
  • Computer Vision
    Instructor-Led Training
    General
    Advanced Technical
    Wednesday, Nov 06
    10:00 a.m. Wednesday, Nov 06

    Anomaly detection is critical in many industries, especially cybersecurity, finance, healthcare, retail and telecom. Variational autoencoders can outperform traditional techniques for anomaly detection. In this course, you'll learn how to:

    · Use Bayesian inference roots of variational autoencoders and their implementation
    · Define anomalies as the probability of being generated from a given model below a certain threshold and set thresholds that are specific to industry or use case
    · Use variational autoencoders to detect anomalies from all data points

    Upon completion, you'll know how to train a variational autoencoder to detect anomalies within the data.

    , Senior Technical Marketing Engineer, NVIDIA

    Primary Topic: Computer Vision
    Submission Type: Instructor-Led Training
    Industry Segments: General
    Audience Level: Advanced Technical
    • Wednesday, Nov 610:00 AM - 11:45 AM EST
      UMD at Ronald Reagan Bldg, Smith School of Business Classroom 3
  • Computer Vision
    Talk
    Government / National Labs
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91519-augmenting-intelligence-with-computer-vision/">View VIDEO</a>
    3:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We'll discuss how we designed our end-to-end AI platform, with a focus on how customer feedback influences our road map. Our platform overview will cover data, training, and deployment functionality. We’ll show how the components of the platform are linked as microservices using gRPC and Kubernetes. Use cases will show how we’re using a broad AI platform with an intuitive and user-friendly interface to improve analyst and customer performance.

    , CEO, Clarifai

    Primary Topic: Computer Vision
    Submission Type: Talk
    Industry Segments: Government / National Labs
    Audience Level: Beginner Technical
    • Tuesday, Nov 53:30 PM - 4:20 PM EST
      Atrium Ballroom A
  • AI & Deep Learning Research
    Talk
    Software
    Intermediate Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91247-automatic-mixed-precision-in-tensorflow/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91247-automatic-mixed-precision-in-tensorflow.pdf">View PDF</a>
    2:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll describe a new feature in TensorFlow that enables mixed precision through the addition of a single line of Python code to training scripts. It is critical to use a mix of single and half precision to obtain peak performance and energy efficiency while maintaining full accuracy. Compared to single precision, half precision makes 2x better use of the available DRAM bandwidth, allows training of deeper network architectures, and boosts raw math throughput up to 8x using Volta Tensor Cores. We’ll describe our approach, which is based on a Grappler graph optimization pass and works with TF 1.x graph-based models as well as with future TensorFlow 2.0 models that make use of tf.function decorators. Empirical results show that result accuracy matches that of a model trained in single-precision, while training speedups are similar to what can be achieved with hand-coded mixed precision strategies.

    , Senior Developer Technology Engineer, NVIDIA

    Primary Topic: AI & Deep Learning Research
    Submission Type: Talk
    Industry Segments: Software
    Audience Level: Intermediate Technical
    • Tuesday, Nov 52:30 PM - 3:20 PM EST
      Amphitheater
  • Accelerated Data Science
    Sponsored Talk
    Developer Tools
    Government / National Labs
    Business/Executive Level
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91475-big-data-platform-the-killer-devsecops-platform-for-federal-ai-projects/">View VIDEO</a>
    1:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll focus on the Department of Defense’s Big Data Platform (BDP), which is being used for multi-domain operations. Alion’s approach to DevSecOps aligns with the DoD’s DevSecOps Reference Design to deliver capabilities rapidly by working across infrastructures to obtain resource efficiencies between the cloud and on-premise infrastructures. In Alion’s implementation of DevSecOps, the BDP is the cohesive intermediary where data is securely stored, normalized, labeled, and made available to internal and external services. It’s now the go-to architecture for massive scale data processing in which AI applications can be rapidly developed, trained, and deployed. The BDP ecosystem allocates between GPU and CPU nodes to manage the compute pipeline to optimize the workload’s infrastructure resource usage and processing performance. A government open source architecture, the BDP is available to the U.S. federal government.

    , Technical Director, Alion

    Primary Topic: Accelerated Data Science
    Submission Type: Sponsored Talk
    Other Topics: Developer Tools
    Industry Segments: Government / National Labs
    Audience Level: Business/Executive Level
    • Wednesday, Nov 61:30 PM - 1:55 PM EST
      Oceanic
  • Accelerated Data Science
    Talk
    General
    Beginner Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91406-blazingsql-the-rapids-sql-engine/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91406-blazingsql-the-rapids-sql-engine.pdf">View PDF</a>
    1:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll present BlazingSQL, RAPIDS’ open source SQL engine. BlazingSQL eliminates the need to build and deploy a database, enabling users to fully integrate high-performance SQL into their RAPIDS workflows.It’s built entirely on the GPU Apache Arrow standard that underpins the RAPIDS ecosystem and the primitives underneath the cuDF and cuIO libraries. BlazingSQL supports a myriad of data sources. Users can query Apache Parquet and JSON in a data lake with in-memory data sources like Apache Arrow or Pandas in a single, intuitive, SQL query that feeds machine learning, deep learning, or graph workloads. We'll launch and run a series of BlazingSQL workloads distributed on a multi-GPU cluster.

    , CEO, BlazingSQL

    , CTO, BlazingSQL

    , VP of Engineering, BlazingSQL

    Primary Topic: Accelerated Data Science
    Submission Type: Talk
    Industry Segments: General
    Audience Level: Beginner Technical
    • Tuesday, Nov 51:30 PM - 1:55 PM EST
      Atrium Ballroom B
  • AI Application/Deployment/Inference
    Talk
    Accelerated Data Science
    Defense
    Intermediate Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91262-building-automatic-modulation-recognition-wireless-systems-using-deep-learning-in-matlab/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91262-building-automatic-modulation-recognition-wireless-systems-using-deep-learning-in-matlab.pdf">View PDF</a>
    3:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll discuss how the use of AI techniques on signals is growing in popularity across a variety of applications. We’ll explore how deep learning techniques in MATLAB can be used to build a predictive model that automatically recognizes the modulation type of a wireless signal. We’ll cover several workflows for building such a model and focus on how MATLAB’s capabilities can address the challenges involved at every stage of building smart wireless systems, from signal acquisition to model deployment.

    , Sr. Product Manager, Signal Processing and Communications, Mathworks

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Talk
    Other Topics: Accelerated Data Science
    Industry Segments: Defense
    Audience Level: Intermediate Technical
    • Tuesday, Nov 53:30 PM - 4:20 PM EST
      Hemisphere B
  • Accelerated Data Science
    AI Application/Deployment/Inference
    High Performance Computing
    Virtual Reality/Augmented Reality
    Talk
    Accelerated Data Science
    AI Application/Deployment/Inference
    Software
    Beginner Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91221-changing-the-way-we-understand-data/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91221-changing-the-way-we-understand-data.pdf">View PDF</a>
    2:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll talk about how AI-driven visualizations, virtual reality, and accelerated data science are revolutionizing the way problems are addressed in the commercial and federal sectors. Advances in computer graphics have made it possible to exploit GPUs for accelerated data science and rendering with tools like NVIDIA’s RAPIDS. We can run complex models and visualize results rapidly. Through GPU-enabled technologies like 3D visualization and virtual reality, we can reach insights faster and collaborate with people all over the world.

    , CTO, Virtualitics Inc

    Primary Topic: Accelerated Data Science, AI Application/Deployment/Inference, High Performance Computing, Virtual Reality/Augmented Reality
    Submission Type: Talk
    Other Topics: Accelerated Data Science, AI Application/Deployment/Inference
    Industry Segments: Software
    Audience Level: Beginner Technical
    • Tuesday, Nov 52:30 PM - 3:20 PM EST
      Horizon
  • Healthcare and Life Sciences
    Talk
    Developer Tools
    High Performance Computing
    Government / National Labs
    Intermediate Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91258-charmm-molecular-dynamics-engine-optimization-on-volta-gpus/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91258-charmm-molecular-dynamics-engine-optimization-on-volta-gpus.pdf">View PDF</a>
    3:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll discuss the Chemistry at Harvard Molecular Mechanics program (CHARMM), one of the oldest and most feature-rich molecular dynamics packages. Its first set of migration to GPUs occurred in 2014. We’ve extended this effort to provide new features and optimized calculations. The previous design targeted a heterogeneous CPU-GPU architecture, while the newer version is optimized for a single GPU system. Multi-GPU utilization can be achieved at a higher level of parallel runs rather than a single run on multiple GPUs. The addition of new integrators, thermostats, and barostats can simulate a variety of ensembles.

    , Postdoctoral Student, NIH

    Primary Topic: Healthcare and Life Sciences
    Submission Type: Talk
    Other Topics: Developer Tools, High Performance Computing
    Industry Segments: Government / National Labs
    Audience Level: Intermediate Technical
    • Wednesday, Nov 63:30 PM - 3:55 PM EST
      Atrium Ballroom A
  • Computer Vision
    Instructor-Led Training
    Healthcare & Life Sciences
    Advanced Technical
    Wednesday, Nov 06
    1:00 p.m. Wednesday, Nov 06

    Prerequisites: Experience with CNNs and Long Short Term Memory (LSTMs)

    Coarse-to-Fine Context Memory (CFCM) is a technique developed for image segmentation using very deep architectures. It incorporates features from many different scales with convolutional Long Short Term Memory (LSTM). You will:

    · Take a deep dive into encoder-decoder architectures for medical image segmentation
    · Get to know common building blocks (convolutions, pooling layers, residual nets, etc.)
    · Investigate different strategies for skip connections

    Upon completion, you'll be able to apply CFCM techniques to medical image segmentation and similar imaging tasks.

    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 courses.nvidia.com/join before you arrive.

    , Deep Learning Solutions Architect, NVIDIA

    Primary Topic: Computer Vision
    Submission Type: Instructor-Led Training
    Industry Segments: Healthcare & Life Sciences
    Audience Level: Advanced Technical
    • Wednesday, Nov 61:30 PM - 3:15 PM EST
      UMD at Ronald Reagan Bldg, Smith School of Business Classroom 2
  • AI Application/Deployment/Inference
    Talk
    AI & Deep Learning Research
    Government / National Labs
    Beginner Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91157-combining-the-power-of-experts-and-deep-learning-to-explore-nasa-and-noaa-data/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91157-combining-the-power-of-experts-and-deep-learning-to-explore-nasa-and-noaa-data.pdf" target="_blank">View PDF</a>
    4:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We'll demonstrate how combining the power of domain experts and deep learning to explore data from NASA and the National Oceanic and Atmospheric Administration’s satellites and models can help advance climate science and weather research. Through concrete working examples, we'll identify three key tools in the application of deep learning: computing resources, domain expertise, and strategies combining domain knowledge and deep learning techniques. Combining these techniques expands the power of existing data and expertise.

    , Associate Research Scientist, NASA GSFC/ UMBC JCET

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Talk
    Other Topics: AI & Deep Learning Research
    Industry Segments: Government / National Labs
    Audience Level: Beginner Technical
    • Tuesday, Nov 54:30 PM - 5:20 PM EST
      Amphitheater
  • AI & Deep Learning Research
    Talk
    Computer Vision
    Government / National Labs
    Intermediate Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91296-computer-vision-for-satellite-imagery-with-few-labels/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/GTC-DC_Reite_no_animations.pdf">View PDF</a>
    10:00 a.m. Wednesday, Nov 06
    Wednesday, Nov 06
    The need for large volumes of data labeled for specific tasks is perhaps the most common obstacle for successfully applying deep learning. We’ll discuss how to apply deep learning on satellite imagery when labeled training data is limited, or even non-existent. First, we’ll show how a state-of-the-art unsupervised algorithm can learn a feature extractor on the noisy and imbalanced xView data set that readily adapts to several tasks: visual similarity search that performs well on both common and rare classes; identifying outliers within a labeled data set; and learning a class hierarchy automatically. Then we’ll demonstrate how GANs can be applied to jointly model images and their labels to generate an expanded training set, and how this may be applied to the data set from ISPRS’s Potsdam 2D Semantic Labeling Contest to improve a follow-on vehicle detection task when training data is limited.

    , Research Scientist, National Geo-Spatial Intelligence Agency

    Primary Topic: AI & Deep Learning Research
    Submission Type: Talk
    Other Topics: Computer Vision
    Industry Segments: Government / National Labs
    Audience Level: Intermediate Technical
    • Wednesday, Nov 610:00 AM - 10:50 AM EST
      Hemisphere B
  • AI Application/Deployment/Inference
    Talk
    AI & Deep Learning Research
    General
    Intermediate Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91249-conversational-ai-inference-deployment-using-tensorrt-inference-server/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91249-conversational-ai-inference-deployment-using-tensorrt-inference-server.pdf">View PDF</a>
    4:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll demonstrate a complete solution of a popular speech pipeline to implement ASR, NLP, and text to speech as an online streaming speech service using the NVIDIA TensorRT Inference Server. The TensorRT Inference Server maximizes GPU utilization and simplifies deploying AI models at scale. It supports all popular AI frameworks, multi-GPU models, enhanced metrics, dynamic batching use cases, and integrates seamlessly into DevOps deployments using Docker, Kubernetes, Prometheus, and Kubeflow. The server can help deploy automatic speech recognition (ASR), natural language processing (NLP), recommendation systems, and object detection. In this conversational AI demonstration, the inference server combines common deep learning frameworks with custom backends and stitches together the pipeline’s additional components using model ensembling. We’ll demonstrate that TRTIS can simplify deep learning deployments in the cloud and data centers.

    , Solutions Architect, NVIDIA

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Talk
    Other Topics: AI & Deep Learning Research
    Industry Segments: General
    Audience Level: Intermediate Technical
    • Wednesday, Nov 64:30 PM - 5:20 PM EST
      Amphitheater
  • Autonomous Vehicles
    Poster
    Cyber Security
    Automotive / Transportation
    Beginner Technical
    https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/conference-posters/nvidia-gtc19-dc-poster-resizing-web-1920x1607-Pierluigi-Pisu.jpg
    We’ll introduce an end-to-end deep learning approach to securing autonomous vehicles. Numerous successful cyberattacks in current vehicles indicate that cybersecurity is now a crucial issue as the automotive market moves toward autonomous and connected cars. We’ve developed a neural network-based approach that can monitor the module’s integrity and take over if an attack is detected. The trained CNN driving model and the autonomous driving algorithm run in parallel, so that one can take over if a cyberattack compromises the other. We’ve implemented this using an autonomous remote-controlled car equipped with a 2D lidar-based self-driving algorithm ⁠— an intrusion detection system deployed to a Jetson TX2 and Ethernet network.

    , Associate Professor, Clemson University

    Primary Topic: Autonomous Vehicles
    Submission Type: Poster
    Other Topics: Cyber Security
    Industry Segments: Automotive / Transportation
    Audience Level: Beginner Technical
    Poster URL: https://www.nvidia.com/content/dam/en-zz/Solutions/gtc/conference-posters/nvidia-gtc19-dc-poster-resizing-web-1920x1607-Pierluigi-Pisu.jpg
  • Virtual Reality/Augmented Reality
    Talk
    AI & Deep Learning Research
    Computer Vision
    General
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91382-creating-the-next-generation-of-ar-and-vr-experiences-with-5g-and-rtx/">View VIDEO</a>
    1:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll explore the next generation of rendering and computer vision services, which will be enabled by Verizon 5G and NVIDIA RTX. Verizon created the first edge-based computer vision and rendering services, expanding the capabilities of mobile devices. We’ll also cover strategies for edge computing and an overview of high-performance 5G services.

    , Chief Engineer XR Edge, VERIZON

    Primary Topic: Virtual Reality/Augmented Reality
    Submission Type: Talk
    Other Topics: AI & Deep Learning Research, Computer Vision
    Industry Segments: General
    Audience Level: Beginner Technical
    • Tuesday, Nov 51:30 PM - 2:20 PM EST
      Horizon
  • Developer Tools
    Talk
    AI & Deep Learning Research
    High Performance Computing
    General
    Intermediate Technical
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91187-cuda-new-features-and-beyond/">View VIDEO</a>
    3:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll discuss the new features of the latest CUDA release and what they mean to clients’ applications and the work they do with GPUs. We’ll also peer ahead at what the future holds for the platform that underlies all GPU programming and applications.

    , CUDA Architect, NVIDIA

    Primary Topic: Developer Tools
    Submission Type: Talk
    Other Topics: AI & Deep Learning Research, High Performance Computing
    Industry Segments: General
    Audience Level: Intermediate Technical
    • Wednesday, Nov 63:30 PM - 4:20 PM EST
      Amphitheater
  • Accelerated Data Science
    Talk
    AI Application/Deployment/Inference
    Intelligent Machines, IoT & Robotics
    Software
    Intermediate Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91165-cusignal-gpu-acceleration-of-scipy-signal/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91165-cusignal-gpu-acceleration-of-scipy-signal.pdf">View PDF</a>
    4:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    The RAPIDS initiative seeks to bring the power and speed of GPU compute to the data scientist. While software developers and engineers alike are familiar with speed increases invoked by moving applications to the GPU, many have struggled in implementing CUDA at the C/C++ level. The core RAPIDS libraries, cuDF, cuML, and cuGraph have bridged the divide between CUDA and Python developers by providing Pandas, Scikit-Learn, and NetworkX familiar APIs for GPU enabled data science with no CUDA required. In this talk, we will extend this software philosophy to a new domain: signal processing. cuSignal ports the popular SciPy signal's API to GPU. By leveraging cuPy - Chainer's GPU accelerated numPy library, Numba - for custom JIT compiled kernels, and the __cuda_device_array__ interface to move seamlessly between the existing RAPIDS libraries, numPy, and PyTorch via DLPack, we show that signal processing applications can reach significant speedups with little to no knowledge of CUDA.

    , Senior Solutions Architect, NVIDIA

    Primary Topic: Accelerated Data Science
    Submission Type: Talk
    Other Topics: AI Application/Deployment/Inference, Intelligent Machines, IoT & Robotics
    Industry Segments: Software
    Audience Level: Intermediate Technical
    • Tuesday, Nov 54:30 PM - 5:20 PM EST
      Atrium Ballroom B
  • Intelligent Video Analytics
    Talk
    Government / National Labs
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91516-damage-detection-disaster-response-using-ai-gis/">View VIDEO</a>
    3:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll present an end-to-end workflow for damage detection and disaster response using Esri’s ArcGIS AI capabilities and NVIDIA GPUs. Identifying damaged buildings and roads following disasters is a key prerequisite in allocating potentially life-saving resources. Manual identification takes significant manpower and time, which are both critical. With the increased accessibility of drone and satellite imagery, along with computer vision models, we’ve made the complete automation of damaged structure detection possible. Our workflow includes imagery access and management; model training and deployment to production; massive inference at scale; and the creation of meaningful geospatial information products.

    , Director, Artificial Intelligence, Esri

    , GeoAI Business Development, ESRI

    Primary Topic: Intelligent Video Analytics
    Submission Type: Talk
    Industry Segments: Government / National Labs
    Audience Level: Beginner Technical
    • Wednesday, Nov 63:30 PM - 4:20 PM EST
      Polaris
  • Accelerated Data Science
    Sponsored Talk
    General
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91512-data-loading-the-next-frontier-in-scale-out-deep-learning/">View VIDEO</a>
    2:00 p.m. Wednesday, Nov 06
    Wednesday, Nov 06
    We’ll explain how to create efficient input pipelines tailored to specific training data. As the number of projects and GPUs rise and data size increases, there’s no common pipeline that can keep GPUs saturated with data. We’ll examine the relationship between training throughput and image representation, and provide guidance on the trade-offs between pre-processing datasets and in-line data processing. Results from a distributed training environment with multiple NVIDIA DGX-1s and a Pure Storage FlashBlade highlight performance impact at scale. We’ll show how to maximize time to accuracy and shipping models.

    , Senior Director, FlashBlade Technology Strategy, Pure Storage

    Primary Topic: Accelerated Data Science
    Submission Type: Sponsored Talk
    Industry Segments: General
    Audience Level: Beginner Technical
    • Wednesday, Nov 62:30 PM - 3:20 PM EST
      Pavilion
  • Accelerated Data Science
    Sponsored Talk
    AI Application/Deployment/Inference
    Healthcare and Life Sciences
    IT Services
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91467-data-management-and-infrastructure-for-enterprise-ai/">View VIDEO</a>
    1:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll explain why next-generation applications built on machine learning and AI require next-generation infrastructure, and present the infrastructure solutions created by NetApp to deliver the highest performance and shortest time-to-value for enterprise initiatives. Attendees will learn about data management throughout the AI development process and deployment options for AI in hybrid cloud environments. NetApp also allows for integration with Ansible, Kubernetes, and KubeFlow. We’ll show examples of how customers are using these capabilities to change the world.

    , Senior Technical Marketing Engineer, NetApp

    Primary Topic: Accelerated Data Science
    Submission Type: Sponsored Talk
    Other Topics: AI Application/Deployment/Inference, Healthcare and Life Sciences
    Industry Segments: IT Services
    Audience Level: Beginner Technical
    • Tuesday, Nov 51:30 PM - 2:20 PM EST
      Hemisphere B
  • Healthcare and Life Sciences
    Instructor-Led Training
    Healthcare & Life Sciences
    Beginner Technical
    Tuesday, Nov 05
    12:00 p.m. Tuesday, Nov 05

    Prerequisites: Basic experience with Python and CNNs

    Medical datasets present special challenges for the application of deep learning. You will:
    · Learn introductory techniques in data augmentation and standardization
    · Experiment with these techniques on a simple medical imaging dataset
    · Validate your techniques by training a convolutional neural network on the augmented dataset

    Upon completion, you'll be able to apply simple data manipulation techniques to your medical imaging datasets.

    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 courses.nvidia.com/join before you arrive.

    , Solutions Architect, NVIDIA

    Primary Topic: Healthcare and Life Sciences
    Submission Type: Instructor-Led Training
    Industry Segments: Healthcare & Life Sciences
    Audience Level: Beginner Technical
    • Tuesday, Nov 512:30 PM - 2:15 PM EST
      UMD at Ronald Reagan Bldg, Smith School of Business Classroom 2
  • AI Application/Deployment/Inference
    Talk
    General
    Beginner Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91352-deep-learning-and-beyond/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91352-deep-learning-and-beyond.pdf">View PDF</a>
    12:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll explain how deep learning and GPU-accelerated data science are delivering breakthrough results across a range of industries and application domains. Attendees will learn about the most effective neural networks for a variety of use cases, the latest GPU-accelerated workflows for data science, and powerful application development tools and libraries. We’ll cover the best practices to manage data, train models, and optimize applications for high-performance production environments.

    , Senior Director, Developer Programs and Deep Learning Institute, NVIDIA

    Primary Topic: AI Application/Deployment/Inference
    Submission Type: Talk
    Industry Segments: General
    Audience Level: Beginner Technical
    • Tuesday, Nov 512:30 PM - 1:20 PM EST
      Amphitheater
  • Developer Tools
    Instructor-Led Training
    AI & Deep Learning Research
    General
    Advanced Technical
    Tuesday, Nov 05
    2:00 p.m. Tuesday, Nov 05

    Prerequisites: Competency in the Python programming language and professional experience training deep learning models in Python

    Learn how to scale deep learning training to multiple GPUs with Horovod, the open-source distributed training framework built by Uber. In this course, you'll:


    · Complete a step-by-step refactor of a Fashion-MNIST classification model to use Horovod and run on four NVIDIA V100 GPUs
    · Understand Horovod's MPI roots and develop an intuition for parallel programming motifs like multiple workers, race conditions, and synchronization
    · Use techniques like learning rate warmups that greatly impact scaled deep learning performance

    Upon completion, you'll be able to use Horovod to effectively scale deep learning training in new or existing code bases.

    , Solutions Architect, NVIDIA

    Primary Topic: Developer Tools
    Submission Type: Instructor-Led Training
    Other Topics: AI & Deep Learning Research
    Industry Segments: General
    Audience Level: Advanced Technical
    • Tuesday, Nov 52:30 PM - 4:15 PM EST
      UMD at Ronald Reagan Bldg, Smith School of Business Classroom 1
  • Healthcare and Life Sciences
    Instructor-Led Workshop
    Computer Vision
    Healthcare & Life Sciences
    Intermediate Technical
    9:00 a.m. Monday, Nov 04
    Monday, Nov 04

    Prerequisites: Familiarity with deep learning concepts and experience using Python

    Assessment Type: Multiple Choice

    Certification Available

    This workshop explores how to apply convolutional neural networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. You’ll learn how to:

    · Perform image segmentation on MRI images to determine the location of the left ventricle
    · Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease
    · Apply CNNs to MRI scans of low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status

    Upon completion, you’ll be able to apply CNNs to MRI scans to conduct a variety of medical tasks.

    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 courses.nvidia.com/join before you arrive.

    , Deep Learning Solutions Architect, NVIDIA

    Primary Topic: Healthcare and Life Sciences
    Submission Type: Instructor-Led Workshop
    Other Topics: Computer Vision
    Industry Segments: Healthcare & Life Sciences
    Audience Level: Intermediate Technical
    • Monday, Nov 49:00 AM - 5:00 PM EST
      UMD at Ronald Reagan Bldg, Smith School of Business Classroom 3
  • Intelligent Video Analytics
    Instructor-Led Workshop
    Computer Vision
    Cyber Security
    Defense
    Government / National Labs
    Advanced Technical
    9:00 a.m. Monday, Nov 04
    Monday, Nov 04

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

    Assessment Type:Code-based 

    Certification available

    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.

    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.

    , Senior Solution Architect, NVIDIA

    Primary Topic: Intelligent Video Analytics
    Submission Type: Instructor-Led Workshop
    Other Topics: Computer Vision, Cyber Security
    Industry Segments: Defense, Government / National Labs
    Audience Level: Advanced Technical
    • Monday, Nov 49:00 AM - 5:00 PM EST
      UMD at Ronald Reagan Bldg, Smith School of Business Classroom 1
  • Intelligent Machines, IoT & Robotics
    Instructor-Led Workshop
    Computer Vision
    General
    Automotive / Transportation
    Defense
    Intermediate Technical
    9:00 a.m. Monday, Nov 04
    Monday, Nov 04

    Prerequisites: Basic familiarity with deep neural networks and basic coding experience in Python or similar language

    Assessment Type: Code-based, multiple choice

    Certification Available

    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.

    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 courses.nvidia.com/join before you arrive.

    , Solutions Architect, NVIDIA

    Primary Topic: Intelligent Machines, IoT & Robotics
    Submission Type: Instructor-Led Workshop
    Other Topics: Computer Vision
    Industry Segments: General, Automotive / Transportation, Defense
    Audience Level: Intermediate Technical
    • Monday, Nov 49:00 AM - 5:00 PM EST
      Polaris
  • Healthcare and Life Sciences
    Talk
    Computer Vision
    Healthcare & Life Sciences
    Beginner Technical
    Recording
    PDF
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91206-deep-multimodal-data-fusion-for-pathology-applications/">View VIDEO</a>
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/pdf/dc91206-deep-multimodal-data-fusion-for-pathology-applications.pdf">View PDF</a>
    4:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We’ll present a variety of different computational paradigms to fuse information from microscopic images of tissue biopsies, corresponding genomic data, and patient and familial histories. Subjective clinical diagnosis is often based on multimodal information from microscopic and molecular information as well as data from patient and familial histories. But most recent work in objective pathology image analysis doesn’t take into account additional information that can influence diagnosis or prognosis. We’ll demonstrate that fusing multimodal information significantly improves survival prediction, characterization, and prognostication. This paradigm can also be used to identify new biomarkers and morphological features that can lead to the development of new grading schemes.

    , Assistant Professor, Harvard Medical School

    Primary Topic: Healthcare and Life Sciences
    Submission Type: Talk
    Other Topics: Computer Vision
    Industry Segments: Healthcare & Life Sciences
    Audience Level: Beginner Technical
    • Tuesday, Nov 54:30 PM - 5:20 PM EST
      Atrium Ballroom A
  • Networking Event
    Government / National Labs
    7:00 p.m. Tuesday, Nov 05
    Tuesday, Nov 05
    We'll bring together innovative leaders from the public sector to discuss innovative technology and the challenges of AI adoption. We'll also discuss NVIDIA's platform for AI.
    Submission Type: Networking Event
    Industry Segments: Government / National Labs
    • Tuesday, Nov 57:00 PM - 9:00 PM EST
      Polaris
  • Autonomous Vehicles
    Talk
    Automotive / Transportation
    Beginner Technical
    Recording
    <a href="https://on-demand.gputechconf.com/gtcdc/2019/video/dc91460-delivering-the-future-of-autonomy/">View VIDEO</a>
    Wednesday, Nov 06
    11:00 a.m. Wednesday, Nov 06
    We’ll demonstrate how autonomous vehicles can meet the demands of today’s mobile world, which is putting stress on global transport systems and requiring delivery within a day or even hours. Autonomous vehicles can operate 24 hours a day with increased efficiency, improving delivery times and bringing down the annual cost of logistics in the U.S. by 45 percent. We’ll explain how government agencies like the U.S. Postal Service, alongside global manufacturers and startups, are transforming the future of delivery.

    , Head of Public & Government Affairs, TuSimple

    , Director Surface Transportation, United States Postal Service (USPS)

    , Technical Marketing, NVIDIA

    Primary Topic: Autonomous Vehicles
    Submission Type: Talk
    Industry Segments: Automotive / Transportation
    Audience Level: Beginner Technical
    • Wednesday, Nov 611:00 AM - 11:50 AM EST
      Amphitheater