Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Alyson K. Fletcher"'
Publikováno v:
IEEE Journal on Selected Areas in Information Theory. 1:884-896
Fitting multivariate autoregressive (AR) models is fundamental for time-series data analysis in a wide range of applications. This article considers the problem of learning a $p$ -lag multivariate AR model where each time step involves a linear combi
Deep generative priors offer powerful models for complex-structured data, such as images, audio, and text. Using these priors in inverse problems typically requires estimating the input and/or hidden signals in a multi-layer deep neural network from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e3a47354f0331664d64aa5b746a1ca0
http://arxiv.org/abs/1911.03409
http://arxiv.org/abs/1911.03409
Autor:
Alyson K. Fletcher, Sundeep Rangan
Publikováno v:
Information and Inference: A Journal of the IMA. 7:531-562
We consider the problem of estimating a rank-one matrix in Gaussian noise under a probabilistic model for the left and right factors of the matrix. The probabilistic model can impose constraints on the factors including sparsity and positivity that a
Publikováno v:
Journal of Statistical Mechanics: Theory and Experiment. 2021:124004
We consider the problem of estimating the input and hidden variables of a stochastic multi-layer neural network (NN) from an observation of the output. The hidden variables in each layer are represented as matrices with statistical interactions along
Publikováno v:
ISIT
Generalized Linear Models (GLMs), where a random vector $\mathbf{x}$ is observed through a noisy, possibly nonlinear, function of a linear transform $\mathbf{z}=\mathbf{Ax}$ arise in a range of applications in nonlinear filtering and regression. Appr
Autor:
Hendrikje Nienborg, Russell A. Poldrack, Thomas Naselaris, Danielle S. Bassett, Nikolaus Kriegeskorte, Konrad P. Kording, Daphna Shohamy, Alyson K. Fletcher, Kendrick Kay
Publikováno v:
Trends in Cognitive Sciences. 22:365-367
Understanding the computational principles that underlie complex behavior is a central goal in cognitive science, artificial intelligence, and neuroscience. In an attempt to unify these disconnected communities, we created a new conference called Cog
Publikováno v:
ISIT
Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text. Inverse problems using such models require solving an inference problem of estimating the input and hidden units of the multi-layer netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6dd78bdcce974e05886f549cd57beb7
We consider the problem of jointly recovering the vector $\boldsymbol{b}$ and the matrix $\boldsymbol{C}$ from noisy measurements $\boldsymbol{Y} = \boldsymbol{A}(\boldsymbol{b})\boldsymbol{C} + \boldsymbol{W}$ , where $\boldsymbol{A}(\cdot)$ is a kn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7edc0f4e51a038fe7cc40dbbf1031de4
http://arxiv.org/abs/1809.00024
http://arxiv.org/abs/1809.00024
Estimating a vector $\mathbf{x}$ from noisy linear measurements $\mathbf{Ax}+\mathbf{w}$ often requires use of prior knowledge or structural constraints on $\mathbf{x}$ for accurate reconstruction. Several recent works have considered combining linea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db04361ae79114baa7d4205803d0ed3f
http://arxiv.org/abs/1806.10466
http://arxiv.org/abs/1806.10466
Publikováno v:
ISIT
Deep generative networks provide a powerful tool for modeling complex data in a wide range of applications. In inverse problems that use these networks as generative priors on data, one must often perform inference of the inputs of the networks from