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GTC DC 2017
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DC7105 - Improving the Brick & Mortar Retail Customer Experience with GPUs There is a clear opportunity for retailers to generate loyalty and increase sale by focusing on the overall customer experience. In this talk, we will describe how we are developing solutions to track customer activity and build profiles based on physical store activity to personalize the in-store shopping experience. We will describe how GPUs and deep learning are used to create these capabilities – all while protecting personal information and privacy. 50 Minute Talk Trung Tran - CEO, Clarcepto Inc
DC7110 - Deep Learning Demystified What is deep learning? In what fields is it useful, and how does it relate to artificial intelligence? Join this session to get a working understanding of deep learning and why this powerful new technology is getting so much attention. Learn how deep neural networks are trained to perform tasks with super-human accuracy, and the challenges organizations face in adopting this new approach. We'll also cover the software, hardware, and training resources that many organizations are using to overcome the challenges and deliver breakthrough results. Finally, we'll review several best practices for your first deep learning project. 50 Minute Talk Will Ramey - Director, Developer Programs, NVIDIA
DC7111 - Accelerating Cyber Threat Detection with GPUs Analyzing vast amounts of enterprise cyber security data to find threats is hard. Cyber threat detection is also a continuous task, and because of financial pressure, companies have to find optimized solutions for this volume of data. We'll discuss the evolution of big data architectures used for cyber defense and how GPUs are allowing enterprises to do better threat detection more efficiently. We'll discuss (1) briefly the evolution of traditional platforms to lambda architectures with new approaches like Apache Kudu to ultimately GPU-accelerated solutions; (2) current GPU-accelerated database, analysis, and visualization technologies (such as Kinetica and Graphistry), and discuss the problems they solve; (3) the need to move beyond traditional table-based data-stores to graphs for more advanced data explorations, analytics, and visualization; and (4) the latest advances in GPU-accelerated graph analytics and their importance all for improved cyber threat detection. 50 Minute Talk Josh Patterson - Director of Applied Solutions Engineering, NVIDIA
DC7112 - CUDA Optimization Tips, Tricks and Techniques Optimizing your code can be one of the most challenging tasks in GPU programming, but also one of the most rewarding: the performance difference between an initial version and well-tuned code can be a factor of 10 or more. Some optimizations can be quite straightforward while others require care and deep understanding of how the code is executing. A particular focus will be on optimization of the CPU part of your code, which is frequently overlooked even though it is often easier to tune and just as effective. Sometimes the biggest obstacle is just knowing what to look for, so we'll cover a range of techniques that everyone from beginners to CUDA ninjas might not have thought of before. 50 Minute Talk Stephen Jones - Principal Software Engineer, NVIDIA
DC7113 - Malware Detection by Eating a Whole EXE Malware is a problem of increasing impact as our society becomes more dependent on an infrastructure of networked computers. Detecting malware is not a trivial task, and numerous companies and startups are researching ways that machine learning could help solve this problem. At the Laboratory for Physical Sciences, we have been researching new ways of applying Deep Learning to this problem. In contrast to most works in the subject area, ours has been done while minimizing the amount of domain knowledge used. We will be sharing our results in training a neural network that processes an entire binary from scratch, giving us a malware detection system that requires no knowledge to train. This gives our approach considerable flexibility, and can be repurposed to new domains with minimal effort. However, it also poses a number of machine learning and engineering challenges to overcome. 50 Minute Talk Jon Barker - Senior Research Scientist, NVIDIA
Robert Brandon - Security Researcher, Booz Allen Hamilton
Edward Raff - Senior Lead Scientist , Booz Allen Hamilton
Jared Sylvester - Lead Scientist , Booz Allen Hamilton
DC7114 - AI: Transforming Your Work and the World Now Artificial Intelligence is changing the world at an accelerating pace. AI is now rapidly moving from its academic roots to revolutionize business and industry through a convergence of technology advances, social transformations, and genuine economic needs. 90 Minute Keynote David Kirk - NVIDIA Fellow, NVIDIA
DC7115 - Panel Discussion: Connection between Basic Research, AI and US Leadership TBA 50 Minute Panel France Córdova - Director, National Science Foundation
DC7117 - Introducing Project Holodeck-2 NVIDIA is committed to the advancement of next-generation Virtual Reality, complete with stunning hi-fidelity, dynamic physical behaviors, and real-time social interactions. Within Holodeck friends will be able to create and share games, families will be able to explore vacation plans & experiences, designers will be able to evaluate new models, and robots will be able to learn new complex tasks. We'll discuss the Holodeck architecture and use-cases. 50 Minute Talk David Weinstein - Director Pro VR, NVIDIA
DC7119 - High Temporal Resolution ISR Data and Synergistic Decisions through Multi-Stream Deep Learning We'll cover research Harris has performed in two applications of deep learning leveraging NVIDIA GPUs. The first topic pertains to high temporal resolution data sets. Many of the "smallsat" providers are offering imagery collections at extremely high temporal resolution. Deep learning provides the ability to automate much of the routine analysis of this data. The second topic will walk through several use cases Harris has developed harnessing multiple input sources for deep learning. Deep learning has been applied to automatic feature extraction for a variety of individual data types such as imagery, point clouds, and video. One of the less explored benefits of deep learning in remote sensing data is the ability to incorporate multiple streams of data into the same neural network, revealing more information than either of the individual data types can provide alone. 50 Minute Talk William Rorrer - Program Manager, Harris
DC7120 - NVIDIA Tools and SDKs for Training and Simulation In addition to making the world's most powerful GPUs, NVIDIA also develops a wide range of software tools and SDKs that can be used to make better training and simulation systems. These include support for multi-GPU configurations, VR, advanced rendering, high-performance computing, virtualization, and deep learning. 50 Minute Talk Tim Woodard - Sr. Solutions Architect, NVIDIA
DC7121 - Multi-User VR Solutions for Enterprise Deployment Deploying PC-based Virtual Reality solutions throughout the Enterprise poses challenges beyond the typical consumer model of one PC driving one headset for one user at one location. For Consumer VR inside the home, the primary user typically owns, maintains, and controls access to both the PC and physical location. Business needs are different. As the number of locations or simultaneous users at each location increases, manageability becomes difficult and unwieldy. Enterprise requirements come into play such as deployment to temporary locations with limited setup/pack up time, limited physical space, robustness, scaling to many concurrent users, multi-user collaboration, remote IT management, configuration control and system image replication. We'll introduce an experimental approach to Multi-User VR deployment based on virtualization techniques that aims to address these Enterprise Use-Case requirements. 50 Minute Talk Tom Kaye - Sr. Solution Architect, NVIDIA
DC7122 - Building the Foundation of America's AI-Enabled Cities Why does building AI Cities matter now for America? Why should US industry and government aggressively develop and deploy AI and deep learning to solve important problems around public safety and operational efficiency in our urban centers? What are the global trends that make this the right time to drive these changes? This talk with cover these topics, and more. 50 Minute Talk Adam Scraba - Global Business Development Lead, NVIDIA
DC7123 - Using Deep Learning for Active Threat Detection In Retail We'll explore how deep learning techniques can be used to transform passive surveillance systems into active threat detection platforms for small and retail businesses. Deep Science is the first to deploy deep learning solutions to spot robberies and assaults as they're occurring in real-time with retail customers. 25 Minute Talk Sean Huver - Founder and CEO, Deep Science
DC7124 - The Convergence of HPC and Artificial Intelligence, Introducing a New Era for Innovation and Scientific Discovery In the next 5 years three important factors are converging to increase in the pace and depth of scientific discovery. High Performance Computing (HPC) systems are evolving toward Exascale class, where throughput for is projected to increase by 50X for traditional simulation assisted science. In that time frame a new class of applications and workflows based on Deep Learning (DL) and Artificial Intelligence (AI) are emerging, where early examples highlight the potential to improve performance by 2 or more orders of magnitude with improved accuracy. Also, there are important new experiments and clinical systems that will increase the volume and resolution of data by 10X or more from sources that range from outer space, to subatomic particles, the human genome and cellular biology. We'll provide an historical overview of the opportunity and challenges we can expect to encounter to seize these new technologies, and then give multiple examples real world problems. 50 Minute Talk Thomas Gibbs - Developer Relations Manager, NVIDIA
DC7126 - Deep Learning in Medical Imaging- applications in opthalmology, radiology and oncology Deep learning, facilitated by advances in hardware, software and algorithms, has emerged as a leading technology in computer vision and image analysis and is being applied to medical imaging with early successes in radiology, oncology, ophthalmology, pathology and others domains. I will share out experiences addressing clinical questions in ophthalmology and radiology. In ophthalmology, we have created an automated system for classifying images for retinopathy of prematurity (ROP) using deep learning while in radiology, we have developed tools for the cancer diagnosis and assessment of response to therapy. 25 Minute Talk Jayashree Kalpathy-Cramer - Director, QTIM lab, MGH/Harvard Medical School
DC7127 - Cross-Domain Face Recognition Solution Based On GPU-Powered Deep Learning and Inference Government agencies and commercial companies today demonstrate high demand for versatile, stable and highly-efficient person identification solutions supporting cross-domain face recognition and person database clusterization in both controlled and uncontrolled scenarios. Now it becomes possible to successfully resolve cross-domain face recognition challenge using deep learning and even tasks of quadratic complexity using GPU-powered inference of CNN-based face recognition algorithms. We'll focus on (I) the concept of the GPU-powered platform for cross-domain face recognition; (II) its essential performance and critical technical characteristics; (III) approach to reaching the demanded efficiency and quality by using the NVIDIA GPU; (IV) provide examples of completed and ongoing projects that demonstrate achieved high performance and quality parameters in real life conditions. 25 Minute Talk Alexander Khanin - CEO, VisionLabs
DC7128 - Current State of Autonomous Video Security at Enterprise Scale In this session, we will explore how UMBO CV is leveraging deep learning techniques and GPUs to scale up how video is analyzed in real-time and at enterprise scale. 25 Minute Talk Shawn Guan - CEO, Umbo CV Inc.
DC7129 - AI at the Edge - Intelligent Machines Artificial Intelligence is impacting almost every part of the industrial and agricultural supply chain. From robots that quickly adapt to build new products to automated vehicles that address last-mile challenges for product delivery to UAVs that can automatically detect failing infrastructure, the world is transitioning from a process that is largely manual to one that is largely automated. In this talk we will discuss how AI and Deep Learning are enabling these advances. We will also analyze a sampling of early successes across different applications. And finally we will describe some of the remaining challenges to wide-scale deployment and work that NVIDIA is doing to address those challenges via the Isaac Initiative. 50 Minute Talk Jesse Clayton - Senior Manager of Product Management for Intelligent Machines, NVIDIA
DC7130 - The Impact of Deep Learning on Radiology - 2017 Update Major advances in computer science are beginning to have an impact on radiology. The rapid achievements in performance for object detection in natural images have enabled these impacts. There has been an explosion of research interest and number of publications regarding the use of deep learning in radiology. In this presentation, I will show examples of how deep learning has led to major performance improvements in radiology image analysis, including image segmentation and computer aided diagnosis. 25 Minute Talk Ronald Summers - Senior Investigator, NIH Clinical Center
DCL7101 - Image Classification with TensorFlow: Radiomics - 1p19q Chromosome Status Classification Thanks to work being performed at Mayo Clinic, approaches using deep learning techniques to detect Radiomics from MRI imaging can lead to more effective treatments and yield better health outcomes for patients with brain tumors. Radiogenomics, specifically Imaging Genomics, refers to the correlation between cancer imaging features and gene expression. Imaging Genomics (Radiomics) can be used to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy. The focus of this lab is detection of the 1p19q co-deletion biomarker using deep learning - specifically convolutional neural networks – using Keras and TensorFlow. What is remarkable about this research and lab is the novelty and promising results of utilizing deep learning to predict Radiomics. 120 Minutes Instructor-Led Lab Hoo Chang Shin - Certified Instructor, NVIDIA Deep Learning Institute
DCL7102 - Deep Learning for Genomics using DragoNN with Keras and Theano In this lab, we use the dragonn toolkit on simulated and real regulatory genomic data, demystify popular DragoNN (Deep RegulAtory GenOmics Neural Network) architectures and provide guidelines for modeling and interpreting regulatory sequence using DragoNN models. We will answer questions such as: When is a DragoNN good choice for a learning problem in genomics? How does one design a high-performance model? And, more importantly, can we interpret these models to discover predictive genome sequence patterns to gain new biological insights? 120 Minutes Instructor-Led Lab Hoo Chang Shin - Certified Instructor, NVIDIA Deep Learning Institute
DCL7103 - Modeling Time Series Data with Recurrent Neural Networks in Keras One important area of current research is the use of deep neural networks to classify or forecast time-series data. Time-series data is produced in large volumes from sensors in a variety of application domains including Internet of Things (IoT), cyber security, data center management and medical patient care. In this lab, you will learn how to create training and testing datasets using electronic health records in HDF5 (hierarchical data format version five) and prepare datasets for use with recurrent neural networks (RNNs), which allows modeling of very complex data sequences. You will then construct a long-short term memory model (LSTM), a specific RNN architecture, using the Keras library running on top of Theano to evaluate model performance against baseline data. 120 Minutes Instructor-Led Lab Robert Keating - Certified Instructor, NVIDIA Deep Learning Institute
DCL7104 - Medical Image Analysis with R and MXNet In this lab you will use the deep learning framework MXNet to train a CNN to infer the volume of the left ventricle of the human heart from a time-series of volumetric MRI data. You will learn how to extend the canonical 2D CNN to be applied to this more complex data and how to directly predict the ventricle volume rather than generating an image classification. In addition to the standard Python API, you will also see how to use MXNet through R, which is an important data science platform in the medical research community. 120 Minutes Instructor-Led Lab Robert Keating - Certified Instructor, NVIDIA Deep Learning Institute
DCL7105 - Image Classification with DIGITS Learn how to leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS. You will walk through the process of data preparation, model definition, model training and troubleshooting. You will use validation data to test and try different strategies for improving model performance using GPUs. On completion of this lab, you will be able to use DIGITS to train a DNN on your own image classification application. 120 Minutes Instructor-Led Lab Larry Brown - Certified Instructor, NVIDIA Deep Learning Institute
DCL7106 - Object Detection with DIGITS This lab explores three approaches to identify a specific feature within an image. Each approach is measured in relation to three metrics: model training time, model accuracy and speed of detection during deployment. On completion of this lab, you will understand the merits of each approach and learn how to detect objects using neural networks trained on NVIDIA DIGITS using real-world datasets. 120 Minutes Instructor-Led Lab Larry Brown - Certified Instructor, NVIDIA Deep Learning Institute
DCL7107 - Neural Network Deployment with DIGITS and TensorRT This lab will show three approaches for deployment. The first approach is to directly use inference functionality within a deep learning framework, in this case NVIDIA DIGITS and Caffe. The second approach is to integrate inference within a custom application by using a deep learning framework API, again using Caffe, but this time through its Python API. The final approach is to use the NVIDIA TensorRT™, which will automatically create an optimized inference run-time from a trained Caffe model and network description file. In this lab, you will learn about the role of batch size in inference performance, as well as various optimizations that can be made in the inference process. You will also explore inference for a variety of different DNN architectures trained in other DLI labs. 120 Minutes Instructor-Led Lab Larry Brown - Certified Instructor, NVIDIA Deep Learning Institute
DCL7108 - Test08182017 detailed description of your lab for use on the GTC website 120 Minutes Instructor-Led Lab Robert Keating - Certified Instructor, NVIDIA Deep Learning Institute
Mahesh kumar - nv, nvidia
DC7108 - Disruptive Changes in Ophthalmology by Deep Learning Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of Electronic Medical Records in medicine has created mountains of Big Data particularly in ophthalmology where many discrete quantitive clinical elements like visual acuity can be tied to rich imaging datasets. In this session, we will explore the transformative nature that GPU acceleration has played in accelerating clinical research and show real-life examples of deep learning applications to ophthalmology in creating new steps forward in automated diagnoses, image segmentation, and computer aided diagnoses. 25 Minute Talk Aaron Lee - Assistant Professor, University of Washington
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