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Building an artificial brain with neuro-inspired deep learning

The field of Computer Vision explores how to make machines understand visual data in various ways. Modern Computer Vision started out with optimizing statistical machine learning methods primarily using hand-crafted "features" for tasks such as Object Detection, Segmentation, and Tracking. Another inspiration behind Computer Vision techniques comes from looking into the biological vision systems, for example by mimicking the brain's deep layers of interconnected neurons as computational layers to accomplish the same tasks. AlexNet's win in the 2012 ImageNet competition solidified the practically of what is now called Deep Learning, and nowadays most techniques are based on such Deep Learning techniques.

Yet, just how biologically inspired are they? Would better knowledge of biological vision improve Deep Learning models further? And if so, what kind of direction would such a futuristic Neural Network take? In this talk, we will first review the recent progress of computer vision and related deep learning models, then we will go through a brief overview of how the biological visual system works, and discuss about building a more brain-inspired neural network, to explore if we are able to answer the three questions above.

Speaker: Cheng-Ping Yu, Phiar

Editor's Note: Location has changed from original post.

Monday, 12/02/19


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Palo Alto Networks

3000 Tannery Way
Santa Clara, CA 95054