Search Results for author: Hayaru Shouno

Found 9 papers, 2 papers with code

Bayesian Sparse Covariance Structure Analysis for Correlated Count Data

no code implementations5 Jun 2020 Sho Ichigozaki, Takahiro Kawashima, Hayaru Shouno

In this paper, we propose a Bayesian Graphical LASSO for correlated countable data and apply it to spatial crime data.

Interpretation of ResNet by Visualization of Preferred Stimulus in Receptive Fields

no code implementations2 Jun 2020 Genta Kobayashi, Hayaru Shouno

In addition, we suggest that some inactive neurons in the first layer of ResNet affect the classification task.

General Classification

Fast Bayesian Restoration of Poisson Corrupted Images with INLA

no code implementations2 Apr 2019 Takahiro Kawashima, Hayaru Shouno

Photon-limited images are often seen in fields such as medical imaging.

A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis

no code implementations16 Oct 2018 Aiga Suzuki, Hayaru Shouno

Modeling of textures in natural images is an important task to make a microscopic model of natural images.

Dimensionality Reduction

B-DCGAN:Evaluation of Binarized DCGAN for FPGA

1 code implementation29 Mar 2018 Hideo Terada, Hayaru Shouno

We are trying to implement deep neural networks in the edge computing environment for real-world applications such as the IoT(Internet of Things), the FinTech etc., for the purpose of utilizing the significant achievement of Deep Learning in recent years.

Binarization Edge-computing

Analysis of dropout learning regarded as ensemble learning

no code implementations20 Jun 2017 Kazuyuki Hara, Daisuke Saitoh, Hayaru Shouno

We find that the process of combining the neglected hidden units with the learned network can be regarded as ensemble learning, so we analyze dropout learning from this point of view.

Ensemble Learning Object Recognition +2

Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure

no code implementations18 Jun 2015 Hiroaki Sasaki, Michael U. Gutmann, Hayaru Shouno, Aapo Hyvärinen

The precision matrix of the linear components is assumed to be randomly generated by a higher-order process and explicitly parametrized by a parameter matrix.

Bayesian Image Restoration for Poisson Corrupted Image using a Latent Variational Method with Gaussian MRF

1 code implementation7 Dec 2014 Hayaru Shouno

In our formulation, we interpret the observation through the Poisson noise channel as a likelihood, and evaluate the bound of it with a Gaussian function using a latent variable method.

Image Restoration

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