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<span style="font-family: Tahoma, Geneva, sans-serif; font-size: 14pt; color: rgb(0, 0, 0);"><b><i>"Approximation and Optimization Theory for Neural Networks"</i></b></span>
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<b>Gerrit Welper</b></div>
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Assistant Professor of Mathematics,</div>
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College of Sciences</div>
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University of Central Florida </div>
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Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic.
<b>All are welcome to attend. </b></div>
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<b>NOTE: In-person attendance is requested.</b> Zoom access is intended for external (non-departmental) participants only. </div>
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<b>https://fsu.zoom.us/j/94273595552 </b></div>
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Zoom Meeting # <b>942 7359 5552 </b></div>
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🎦 Colloquium recordings will be made available here, <a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Cf198bbdbb9a3407fe67608dcd335d793%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638617475414008860%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=pjkileQ6vNWC6so3zjiHE3hQmJlefQ3TPexgW6XlyPA%3D&reserved=0" originalsrc="https://www.sc.fsu.edu/colloquium" shash="SytDrjRa877hfTdoauR2hsQ0k1v779baQftIaRJGt/8EUdYbSCoAv8Kys2nSX+BGMMN8jRceR3tP6+zcJSwh/kWuvlU/Us0YtosQpyjbWi/mlSYcASL2uozyy1+H7bmRVipQWeGydoLWtkvpuMRpZbsdQAMmqTrCUdK/b4Hruss=">
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<b>Wednesday, Sep 18</b>, 2024, Schedule: </div>
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* 3:00 to 3:30 PM Eastern Time (US and Canada) </div>
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☕ Nespresso & Teatime - 417 DSL Commons </div>
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<b>* 3:30 to 4:30 PM Eastern Time (US and Canada) </b></div>
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<b>🕟 Colloquium - 499 DSL Seminar Room </b></div>
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<b>Abstract: </b></div>
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The error analysis of neural networks is often split into three components: the approximation error, describes how well the networks can approximate functions, given infinite data. The estimation error describes the additional error contributions from sampling
and the optimization error describes the contributions from the numerical optimizers.</div>
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In particular, the latter is best understood in severely over-parametrized regimes, where we have more samples than width and the networks show an almost linear behavior dominated by the neural tangent kernel (NTK). Many practical networks, however, have less
width than samples and therefore, we extend the theory to the under-parametrized regime. This requires a more careful consideration of approximation errors and results in a unified theory for all three error components.</div>
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Since the NTK theory cannot adequately capture the nonlinear nature of the networks, we also provide some first steps towards a nonlinear theory by comparing trained simple model networks with known results form nonlinear approximation.</div>
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Additional colloquium details can be found here, </div>
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<a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1814-colloquium-with-gerrit-welper-2024-09-18&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Cf198bbdbb9a3407fe67608dcd335d793%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638617475414008860%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=VPkVsBo70xpBxxcmH0w0SiQfpyt0ho4lejlBWhYWjt4%3D&reserved=0" originalsrc="https://www.sc.fsu.edu/news-and-events/colloquium/1814-colloquium-with-gerrit-welper-2024-09-18" shash="teDk8wBNs/5HyFXF3Jckt9DIx9ELOq3uzC+Tsxbl3mlfXVS+5QDHW9ZHfu6dyLN3NIAUZSofqnJNCmqzmpZbtBrVUhMa0JmXNXSxFN9VjJYKpXEhTXahE0BoLQ/08DMYr9haJ3zethJqh0mRGHmf/OHjKpa6pY7Yl65FVFiHxbI=">https://www.sc.fsu.edu/news-and-events/colloquium/1814-colloquium-with-gerrit-welper-2024-09-18</a></div>
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