De Hamid Krim
Machine Learning (ML) has reached an unprecedented performance in various inference problems arising in practice. The sample complexity and that of the model have, however, increasingly emerged as a serious limitation. Given the importance of a number of problems where these issues are central, we have revisited the Conv-net fundamental principle and have reformulated it from a Volterra Series perspective using a polynomial functional paradigm*. We propose a computational Convolutional Network solution which requires no activation function and provides a very competitive inference performance (often better) at a fraction of the sample and model complexity of the most competitive CNN architecture. * Homogeneous Polynomial Functional were first developed and formalized by Frechet
De Eero Simoncelli
De Naoki Saito
De Maarten de Hoop
De Martin Vetterli
De Maureen Clerc
De Irène Waldspurger
De Jérôme Kalifa
De Gitta Kutyniok
De Michael Unser
De Edouard Oyallon
De Remi Gribonval
De Viviane Pons
De Wolfgang Arendt
De David Hewett
De Gérard Ben Arous
De Viviane Pons
