<span class="var-sub_title">Applications of Deep Learning in Industry and Research</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Applications of Deep Learning in Industry and Research


Authors: Lucas Wilson (Dell EMC), Antonio Cisternino (University of Pisa), Matthew Grover (Walmart Inc), Alex Sutton (Microsoft Corporation)

Abstract: The inaugural Applications of Deep Learning in Industry and Research BoF will explore in-production and pre-production uses of deep learning (DL) across industry segments and research domains for various customer/scientist-facing or back-of-office/lab applications. We invite all data scientists, decision makers, researchers and analysts to discuss the need or desire to apply DL in their businesses or research projects, the technical challenges faced when implementing DL-based solutions, the potential value DL-based solutions have provided or may provide, lessons learned in adding deep learning to existing data center operations and applications workflows, and desires for addressing current gaps and future needs.

Long Description: Deep Learning (DL) is quickly becoming essential for businesses in all industry verticals and in many areas of research. The application of DL to various business problems is one of the most important technical challenges companies now face to ensure market competitiveness and provide ongoing value to their customers. Likewise, the application of DL to scientific data promises to enable new discoveries and accelerate understanding of complex phenomena, many of which will then drive innovation in related industry verticals.

The inaugural Applications of Deep Learning in Industry and Research BoF will explore in-production and pre-production uses of deep learning (DL) across industry segments and research domains for various customer/scientist-facing or back-of-office/lab applications. We invite all data scientists, decision makers, researchers and analysts to discuss the need or desire to apply DL in their businesses or research projects, the technical challenges faced when implementing DL-based solutions, and the potential value DL-based solutions have provided or may provide, lessons learned in adding deep learning to existing data center operations and applications workflows, and desires for addressing current gaps and future needs.

We will ask audience members to complete preliminary and wrap-up surveys to help gauge community perceptions of potential benefits and challenges of incorporating DL into their business and research processes. We will make the results of the surveys available via website and use the survey results to guide follow-on conversations with the community. In subsequent years, we hope (should the BoF be accepted) to share progress on relevant survey results.





Back to Birds of a Feather Archive Listing