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A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
The fundamental building block of a CNN is a trainable filter, represented as a discrete grid, used to perform convolution on discrete input data.
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields including computer vision and image processing.
A Dimensionality Reduction Approach for Convolutional Neural Networks
The focus of this paper is the application of classical model order reduction techniques, such as Active Subspaces and Proper Orthogonal Decomposition, to Deep Neural Networks.
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