Doctor of Crosswise: Reducing Over-parametrization in Neural Networks

05/24/2019
by   J. D. Curtó, et al.
0

Dr. of Crosswise proposes a new architecture to reduce over-parametrization in Neural Networks. It introduces an operand for rapid computation in the framework of Deep Learning that leverages learned weights. The formalism is described in detail providing both an accurate elucidation of the mechanics and the theoretical implications.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/12/2022

The Mori-Zwanzig formulation of deep learning

We develop a new formulation of deep learning based on the Mori-Zwanzig ...
research
05/22/2018

Opening the black box of deep learning

The great success of deep learning shows that its technology contains pr...
research
12/21/2020

Towards the Localisation of Lesions in Diabetic Retinopathy

Convolutional Neural Networks (CNN) has successfully been used to classi...
research
02/16/2021

Topological Deep Learning: Classification Neural Networks

Topological deep learning is a formalism that is aimed at introducing to...
research
06/13/2019

Factors for the Generalisation of Identity Relations by Neural Networks

Many researchers implicitly assume that neural networks learn relations ...
research
11/01/2018

How the fundamental concepts of mathematics and physics explain deep learning

Starting from the Fermat's principle of least action, which governs clas...
research
05/17/2016

Deep Action Sequence Learning for Causal Shape Transformation

Deep learning became the method of choice in recent year for solving a w...

Please sign up or login with your details

Forgot password? Click here to reset