Marianna Pensky

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Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel and(More)
The paper considers regression problems with univariate design points. The design points are irregular and no assumptions on their distribution are imposed. The regression function is retrieved by a wavelet based reproducing kernel Hilbert space (RKHS) technique with the penalty equal to the sum of blockwise RKHS norms. In order to simplify numerical(More)
Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a(More)
Hence the problem of estimating g in (1.2) is called a deconvolution problem. The problem arises in many applications [see, e.g., Desouza (1991), Louis (1991), Zhang (1992)] and, therefore, it was studied extensively in the last decade. The most popular approach to the problem was to estimate p x by a kernel estimator and then solve equation (1.2) using a(More)
We consider a problem of recovering a high-dimensional vector μ observed in white noise, where the unknown vector μ is assumed to be sparse. The objective of the paper is to develop a Bayesian formalism which gives rise to a family of l0-type penalties. The penalties are associated with various choices of the prior distributions πn(·) on the number of(More)
The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion(More)
The problem of estimating the log-spectrum of a stationary Gaussian time series by Bayesianly induced shrinkage of empirical wavelet coefficients is studied. A model in the wavelet domain that accounts for distributional properties of the log-periodogram at levels of fine detail and approximate normality at coarse levels in the wavelet decomposition, is(More)
The objective of the paper is to develop a truly functional fully Bayesian method which allows to identify differentially expressed genes in a time-course microarray experiment. Each gene expression profile is modeled as an expansion over some orthonormal basis with coefficients and the number of basis functions estimated from the data. The proposed(More)