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         <div class="speaker-title"><b>A Tensor Network Approach to Equivariant Polynomial Networks</b></div>
         <div class="speaker-abstract">In this talk, we will investigate a coordinate-free attempt to formalize polynomial networks, stemming from the representation of polynomial maps as tensor networks. We will discuss how this framework encompasses previously proposed polynomial networks and adheres to the compositional architecture paradigm of deep learning. Ultimately, given group representations on the input and output spaces, we will see that this discussion naturally extends to the equivariant setting and elaborate on implementation aspects.</div>
-        <div class="speaker-bio">Alexandros Georgiou is a second year Ph.D. student at the National and Kapodistrian University of Athens, under the supervision of Yannis Panagakis, and a fellow of the Archimedes research unit. Previously, he obtained his diploma and M.Sc. from the School of Applied Mathematical and Physical Sciences of the National Technical University of Athens. His main research interests lie in the areas of geometric deep learning and tensor methods.</div>
+        <div class="speaker-bio">Alexandros Georgiou is a second year Ph.D. student at the National and Kapodistrian University of Athens, under the supervision of Yannis Panagakis, and a fellow of the Archimedes research unit/ Αthena Research Center. Previously, he obtained his diploma and M.Sc. from the School of Applied Mathematical and Physical Sciences of the National Technical University of Athens. His main research interests lie in the areas of geometric deep learning and tensor methods.</div>
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         <img class="avatar" src="https://assets.amazon.science/dims4/default/362fb24/2147483647/strip/true/crop/684x925+74+0/resize/340x460!/format/webp/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Ff4%2F73%2F65c9a02249bca0b9d56f5e52f2ad%2Fandrew-gordon-wilson.jpg">