News
February 2023: We are thankful to all the participants for their thoughtful questions and remarks. As a remark, you can find various implementations in PyTorch, Keras, TensorFlow and JAX in this site shared during the tutorial.
December 2022: The site of the previous version of the tutorial (along with the respective material) can be found in this link.
Overview
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 state-of-the-art performance in a range of tasks. Despite the fact that polynomial networks have appeared for several decades in machine learning and complex systems, they are not widely acknowledged for their role in modern deep learning.
In this tutorial we intend to bridge the gap and draw parallelisms between modern deep learning approaches and polynomial networks. To this end, we will share recent developments on the topic, as well as explain the required tools.
Schedule Detail
Tentative schedule.
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8.30 AM
Introduction
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9.10 AM
High-degree polynomial expansions
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10.30 AM
Break
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10.50 AM
Recognition and generation with polynomial nets
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11.50 AM
Coding exampls with Jupyter notebooks
VENUE
AAAI 2023, Washington, DC, USA
Walter E. Washington Convention Center, Mount Vernon Place Northwest
FAQ
Organizers
Grigorios Chrysos
EPFL
Markos Georgopoulos
Imperial College London
Razvan Pascanu
Deepmind
Volkan Cevher
EPFL