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DESCRIPTION:Speaker: Dr. Alejandro Parada-Mayorga, Postdoctoral Researcher, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia
Talk Title: Algebraic Neural Networks: Stability to Deformations
Abstract: Convolutional architectures play a central role on countless scenarios in machine learning, and the numerical evidence that proves the advantages of using them is overwhelming. Theoretical insights have provided solid explanations about why such architectures work well. These analysis apparently different in nature, have been performed considering signals defined on different domains and with different notions of convolution, but with remarkable similarities in the final results, posing then the question of whether there exists an explanation for this at a more structural level. In this talk we provide an affirmative answer to this question with a first principles analysis introducing algebraic neural networks (AlgNNs), which rely on algebraic signal processing and algebraic signal models. In particular, we study the stability properties of algebraic neural networks showing that stability results for traditional CNNs, graph neural networks (GNNs), group neural networks, graphon neural networks, or any formal convolutional architecture, can be derived as particular cases of our results. This shows that stability is a universal property - at an algebraic level - of convolutional architectures, and this also explains why the remarkable similarities we find when analyzing stability for each particular type of architecture.
Biography: Alejandro Parada-Mayorga (alejopm@seas.upenn.edu) received his B.Sc. and M.Sc. degrees in electrical engineering from Universidad Industrial de Santander, Colombia, in 2009 and 2012, respectively, and his Ph.D. degree in electrical engineering from the University of Delaware, Newark, 2019. Currently, he is a postdoctoral researcher at the University of Pennsylvania, Philadelphia, under the supervision of Prof. Alejandro Ribeiro. His research interests include algebraic signal processing, algebraic neural networks, graph neural networks, graph signal processing, and applications of representation theory of algebras and category theory.
Host: Dr. Shri Narayanan, shri@ee.usc.edu
Webcast: https://usc.zoom.us/j/92088625170?pwd=enhYNUpicEYvS0R5SEViVVBobjQ1dz09
SEQUENCE:5
DTSTART:20220309T100000
LOCATION:EEB 248
DTSTAMP:20220309T100000
SUMMARY:ECE Seminar: Algebraic Neural Networks: Stability to Deformations
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DTEND:20220309T110000
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