7,888 research outputs found
A Comparison of Algorithms for Learning Hidden Variables in Normal Graphs
A Bayesian factor graph reduced to normal form consists in the
interconnection of diverter units (or equal constraint units) and
Single-Input/Single-Output (SISO) blocks. In this framework localized
adaptation rules are explicitly derived from a constrained maximum likelihood
(ML) formulation and from a minimum KL-divergence criterion using KKT
conditions. The learning algorithms are compared with two other updating
equations based on a Viterbi-like and on a variational approximation
respectively. The performance of the various algorithm is verified on synthetic
data sets for various architectures. The objective of this paper is to provide
the programmer with explicit algorithms for rapid deployment of Bayesian graphs
in the applications.Comment: Submitted for journal publicatio
Medical image denoising using convolutional denoising autoencoders
Image denoising is an important pre-processing step in medical image
analysis. Different algorithms have been proposed in past three decades with
varying denoising performances. More recently, having outperformed all
conventional methods, deep learning based models have shown a great promise.
These methods are however limited for requirement of large training sample size
and high computational costs. In this paper we show that using small sample
size, denoising autoencoders constructed using convolutional layers can be used
for efficient denoising of medical images. Heterogeneous images can be combined
to boost sample size for increased denoising performance. Simplest of networks
can reconstruct images with corruption levels so high that noise and signal are
not differentiable to human eye.Comment: To appear: 6 pages, paper to be published at the Fourth Workshop on
Data Mining in Biomedical Informatics and Healthcare at ICDM, 201
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