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Found 10 papers, 0 papers with code
Geometry of EM and related iterative algorithms
The Expectation--Maximization (EM) algorithm is a simple meta-algorithm that has been used for many years as a methodology for statistical inference when there are missing measurements in the observed data or when the data is composed of observables and unobservables.
Fast and robust multiplane single molecule localization microscopy using deep neural network
Single molecule localization microscopy is widely used in biological research for measuring the nanostructures of samples smaller than the diffraction limit.
On a convergence property of a geometrical algorithm for statistical manifolds
In this paper, we examine a geometrical projection algorithm for statistical inference.
The global optimum of shallow neural network is attained by ridgelet transform
We prove that the global minimum of the backpropagation (BP) training problem of neural networks with an arbitrary nonlinear activation is given by the ridgelet transform.
Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks
The feature map obtained from the denoising autoencoder (DAE) is investigated by determining transportation dynamics of the DAE, which is a cornerstone for deep learning.
Transport Analysis of Infinitely Deep Neural Network
Starting from the shallow DAE, this paper develops three topics: the transport map of the deep DAE, the equivalence between the stacked DAE and the composition of DAEs, and the development of the double continuum limit or the integral representation of the flow representation.
Double Sparse Multi-Frame Image Super Resolution
A large number of image super resolution algorithms based on the sparse coding are proposed, and some algorithms realize the multi-frame super resolution.
Neural Network with Unbounded Activation Functions is Universal Approximator
This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified linear unit (ReLU), which is the new de-facto standard of deep learning.
Sparse Coding Approach for Multi-Frame Image Super Resolution
Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method.
Nonparametric Weight Initialization of Neural Networks via Integral Representation
A new initialization method for hidden parameters in a neural network is proposed.
