6 The convolution layer 13 6.1 What is a convolution? . . . . . . . . . . . . . . . . . . . . . . . . 13 6.2 Why to convolve? . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 6.3 Convolution as matrix product . . . . . . . . . . . . . . . . . . . 18 6.4 The Kronecker product . . . . . . . . . . . . . . . . . . . . . . . 20 6.5 Backward propagation: update the parameters . . . . . . . . ...

We denote each fully-connected layer fc(d) by its output dimension d, and volumetric convolution layer by conv3D(k, c, s) representing kernel size of k, strides of s across three spatial axes, and c channels. 2D convolutional layer is represented as conv2D(k, c, s). and the volumetric transpose convolution layer by deconv3D(k, c, s). Encoder and Generator for Aligned Shapes The variational alig...

Abstract. R-limited functions are multivariate generalization of band-limited functions whose Fourier transforms are supported within a compact region R ⊂ Rn. In this work, we generalize sampling and interpolation theorems for band-limited functions to R-limited functions. More precisely, we investigated the following question: “For a function compactly supported within a region similar to R, d...

6 The convolution layer 11 6.1 What is convolution? . . . . . . . . . . . . . . . . . . . . . . . . . 11 6.2 Why to convolve? . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.3 Convolution as matrix product . . . . . . . . . . . . . . . . . . . 15 6.4 The Kronecker product . . . . . . . . . . . . . . . . . . . . . . . 17 6.5 Backward propagation: update the parameters . . . . . . . . ...

Min-plus convolution is an algebra system that has applications to computer networks. Mathematically, the identity of min-plus convolution plays a key role in theory. On the other hand, the mathematical representation of the identity, which is computable with digital computers, is essential for further developing min-plus convolution (e.g., de-convolution) in practice. However, the identical el...

In this paper, we deal with the convolution series that are a far reaching generalization of conventional power and fractional exponents including Mittag-Leffler type functions. Special attention is given to most interesting case generated by Sonine kernels. first formulate prove second fundamental theorem for general integrals $n$-fold sequential derivatives both Riemann-Liouville Caputo types...

Convolution semigroups of states on a quantum group form the natural noncommutative analogue of convolution semigroups of probability measures on a locally compact group. Here we initiate a theory of weakly continuous convolution semigroups of functionals on a C∗-bialgebra, the noncommutative counterpart of a locally compact semigroup. On locally compact quantum groups we obtain a bijective cor...

Translation and convolution associated with discrete wavelet transform are investigated using properties of Calderón-Zygmund operator and Riesz fractional integral operator. Dual convolution is also studied. The wavelet convolution is applied to approximate functions belonging to certain p L-spaces.

Convolutional neural networks (CNNs) are a popular and highly performant choice for pixel-wise dense prediction or generation. One of the commonly required components in such CNNs is a way to increase the resolution of the network’s input. The lower resolution inputs can be, for example, low-dimensional noise vectors in image generation [7] or low resolution (LR) feature maps for network vis...