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Found 9 papers, 2 papers with code
Bayesian Image Restoration for Poisson Corrupted Image using a Latent Variational Method with Gaussian MRF
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.
Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
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.
Analysis of dropout learning regarded as ensemble learning
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.
B-DCGAN:Evaluation of Binarized DCGAN for FPGA
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.
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-
Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks.
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis
Modeling of textures in natural images is an important task to make a microscopic model of natural images.
Fast Bayesian Restoration of Poisson Corrupted Images with INLA
Photon-limited images are often seen in fields such as medical imaging.
Interpretation of ResNet by Visualization of Preferred Stimulus in Receptive Fields
In addition, we suggest that some inactive neurons in the first layer of ResNet affect the classification task.
Bayesian Sparse Covariance Structure Analysis for Correlated Count Data
In this paper, we propose a Bayesian Graphical LASSO for correlated countable data and apply it to spatial crime data.
