BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Lars Ruthotto (Emory University) DTSTART:20200701T201500Z DTEND:20200701T204000Z DTSTAMP:20260423T040151Z UID:SciDL/9 DESCRIPTION:Title: De ep Neural Networks Motivated by PDEs\nby Lars Ruthotto (Emory Universi ty) as part of Workshop on Scientific-Driven Deep Learning (SciDL)\n\n\nAb stract\nOne of the most promising areas in artificial intelligence is deep learning\, a form of machine learning that uses neural networks containin g many hidden layers. Recent success has led to breakthroughs in applicati ons such as speech and image recognition. However\, more theoretical insig ht is needed to create a rigorous scientific basis for designing and train ing deep neural networks\, increasing their scalability\, and providing in sight into their reasoning. This talk bridges the gap between partial diff erential equations (PDEs) and neural networks and presents a new mathemati cal paradigm that simplifies designing\, training\, and analyzing deep neu ral networks. It shows that training deep neural networks can be cast as a dynamic optimal control problem similar to path-planning and optimal mass transport. The talk outlines how this interpretation can improve the effe ctiveness of deep neural networks. First\, the talk introduces new types o f neural networks inspired by to parabolic\, hyperbolic\, and reaction-dif fusion PDEs. Second\, the talk outlines how to accelerate training by expl oiting reversibility properties of the underlying PDEs.\n LOCATION:https://researchseminars.org/talk/SciDL/9/ END:VEVENT END:VCALENDAR