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Wednesday, Feb 7, 2018
Building Intuition into Adversarial Examples in Deep Learning
Speaker:
Dr. Simant Dube, AI Winning Solutions
Location:
** WARNING: venue is not our usual AMD location **
Intel SC12 Auditorium, 3600 Juliette Ln, Santa Clara, CA 95054 (Google Maps)
Need visitor registration to enter this facility. Please register here.
Schedule:
6:30pm-7:00pm: Registration, Food, Networking
7:00pm-8:00pm: Talk
8:00pm-8:30pm: Q&A and Networking
Cost:
FREE for IEEE members
$5 for non-members (pay at door)
Abstract:
In recent years, deep neural networks have found wide ranging applications in the general field of AI. However, it has been shown that deep learning suffers from Adversarial Examples which have puzzled AI scientists. For acceptance of deep learning based AI solutions, it is important to understand this intriguing phenomenon and to eliminate it. Furthermore, there has been debate in social media about urgent need to bring rigor to the field of deep learning. Inspired by that, in this talk, we present novel results which explain adversarial examples in Computer Vision and which also pave way for future progress in AI.
Biography:
Dr. Simant Dube is a computer scientist and engineer with interests in Machine Learning, Computer Vision, AI and Data Science. Most recently he worked as Senior Principal Engineer at Bossa Nova Robotics and as Faculty Member at New College of Florida, the honors college of Florida. He has worked in industry building successful products, such as automatic analysis of protein crystal images, statistical analysis of copy number variation in biotechnology, firefly technology to recognize millions of products with smartphone camera, automatic analysis of images of aisles of retail supermarkets, and others. Currently, he is working with startup incubators and academic institutes in the field of AI.
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