First tutorial on polynomial networks at CVPR'22
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Razvan Pascanu
Jun 14, 2022, 11:37:19 AM6/14/22
to women-in-mac...@googlegroups.com, Cevher Volkan, Chrysos Grigorios, Markos Georgopoulos
Dear All,
Sorry for cross-posting.
For those participating to CVPR'22 we want to advertise the first tutorial on polynomial networks.
Date: 20th June 2022, 1 - 5 pm (CDT).
Place: Ernest N. Morial Convention Center, New Orleans, USA.
Site: https://polynomial-nets.github.io/
Background: Polynomial networks enable a new network design that treats a network as a high-degree polynomial expansion of the input. Recently, polynomial networks have demonstrated competitive performance in a range of tasks, including image classification, reinforcement learning, non-euclidean representation learning, (conditional) image generation, and sequence models. Despite the fact that polynomial networks have appeared for several decades in machine learning and complex systems, theyare not widely acknowledged for their role in modern deep learning.
Goals of the tutorial:
a) Draw parallelisms between modern deep learning approaches and polynomial networks,
b) Introduce a wider audience to this paradigm for designing architectures. That makes our tutorial accessible to both early career practitioners and established researchers.
We hope to see you there!
Sorry for cross-posting.
For those participating to CVPR'22 we want to advertise the first tutorial on polynomial networks.
Date: 20th June 2022, 1 - 5 pm (CDT).
Place: Ernest N. Morial Convention Center, New Orleans, USA.
Site: https://polynomial-nets.github.io/
Background: Polynomial networks enable a new network design that treats a network as a high-degree polynomial expansion of the input. Recently, polynomial networks have demonstrated competitive performance in a range of tasks, including image classification, reinforcement learning, non-euclidean representation learning, (conditional) image generation, and sequence models. Despite the fact that polynomial networks have appeared for several decades in machine learning and complex systems, theyare not widely acknowledged for their role in modern deep learning.
Goals of the tutorial:
a) Draw parallelisms between modern deep learning approaches and polynomial networks,
b) Introduce a wider audience to this paradigm for designing architectures. That makes our tutorial accessible to both early career practitioners and established researchers.
We hope to see you there!
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