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...

The conditional tail expectation in risk analysis describes the expected amount of risk that can be experienced given that a potential risk exceeds a threshold value, and provides an important measure for right-tail risk. In this paper, we study the convolution and extreme values of dependent risks that follow a multivariate phase type distribution, and derive explicit formulas of several condi...

Associative memories are conventionally used to represent data with very simple structure: sets of pairs of vectors. This paper describes a method for representing more complex compositional structure in distributed representations. The method uses circular convolution to associate items, which are represented by vectors. Arbitrary variable bindings, short sequences of various lengths, simple f...

A solution to the problem of representing compositional structure using distributed representations is described. The method uses circular convolution to associate items, which are represented by vectors. Arbitrary variable bindings, short sequences of various lengths, frames, and reduced representations can be compressed into a xed width vector. These representations are items in their own rig...

Let the measure algebra of a topological group G be equipped with the topology of uniform convergence on bounded right uniformly equicontinuous sets of functions. Convolution is separately continuous on the measure algebra, and it is jointly continuous if and only if G has the SIN property. On the space LUC(G)∗ which includes the measure algebra, convolution is also jointly continuous if and on...