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Chapter and Conference Paper
Variational Autoencoders for Precoding Matrices with High Spectral Efficiency
Neural networks are used for channel decoding, channel detection, channel evaluation, and resource management in multi-input and multi-output (MIMO) wireless communication systems. In this paper, we consider t...
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Chapter and Conference Paper
Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximizes the lower bound of its marg...
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Chapter and Conference Paper
A Simple Method to Evaluate Support Size and Non-uniformity of a Decoder-Based Generative Model
Theoretical analysis in [1] suggested that adversarially trained generative models are naturally inclined to learn distribution with low support. In particular, this effect is caused by the limited capacity of t...
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Chapter and Conference Paper
Multi-utility Learning: Structured-Output Learning with Multiple Annotation-Specific Loss Functions
Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training. In this paper we try to overcome this diffic...
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Chapter and Conference Paper
Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data
In the paper we address a challenging problem of incorporating preferences on possible shapes of an object in a binary image segmentation framework. We extend the well-known conditional random fields model by ...
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Chapter and Conference Paper
Submodular Relaxation for MRFs with High-Order Potentials
In the paper we propose a novel dual decomposition scheme for approximate MAP-inference in Markov Random Fields with sparse high-order potentials, i.e. potentials encouraging relatively a small number of varia...
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Chapter and Conference Paper
Image Segmentation with a Shape Prior Based on Simplified Skeleton
In the paper we propose a new deformable shape model that is based on simplified skeleton graph. Such shape model allows to account for different shape variations and to introduce global constraints like known...
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Chapter and Conference Paper
An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice
We consider the problem of statistical analysis of gene expression in a mouse brain during cognitive processes. In particular we focus on the problems of anatomical segmentation of a histological brain slice a...
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Chapter and Conference Paper
3-D Mouse Brain Model Reconstruction from a Sequence of 2-D Slices in Application to Allen Brain Atlas
The paper describes a method of fully automatic 3D-reconstruction of a mouse brain from a sequence of histological coronal 2D slices. The model is constructed via non-linear transformations between the neighbo...
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Chapter and Conference Paper
ODDboost: Incorporating Posterior Estimates into AdaBoost
Boosting methods while being among the best classification methods developed so far, are known to degrade performance in case of noisy data and overlap** classes. In this paper we propose a new upper general...
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Chapter and Conference Paper
Fuzzy Rules Generation Method for Pattern Recognition Problems
In the paper we consider the problem of automatic fuzzy rules mining. A new method for generation of fuzzy rules according to the set of precedents is suggested. The proposed algorithm can find all significant...
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Chapter and Conference Paper
The Use of Stability Principle for Kernel Determination in Relevance Vector Machines
The task of RBF kernel selection in Relevance Vector Machines (RVM) is considered. RVM exploits a probabilistic Bayesian learning framework offering number of advantages to state-of-the-art Support Vector Mach...
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Chapter and Conference Paper
The Use of Bayesian Framework for Kernel Selection in Vector Machines Classifiers
In the paper we propose a method based on Bayesian framework for selecting the best kernel function for supervised learning problem. The parameters of the kernel function are considered as model parameters and...
