Search Results - "Fletcher, Alyson K."

Bilinear Recovery Using Adaptive Vector-AMP by Sarkar, Subrata, Fletcher, Alyson K., Rangan, Sundeep, Schniter, Philip

“…We consider the problem of jointly recovering the vector band the matrix C from noisy measurements Y = A(b)C + W,where A(·) is a known affine linear function…”
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Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing by Rangan, S., Fletcher, A. K., Goyal, V. K.

“…The replica method is a nonrigorous but well-known technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems…”
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Vector Approximate Message Passing by Rangan, Sundeep, Schniter, Philip, Fletcher, Alyson K.

“…The standard linear regression (SLR) problem is to recover a vector <inline-formula> <tex-math notation="LaTeX">\mathrm {x}^{0} </tex-math></inline-formula>…”
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On the Convergence of Approximate Message Passing With Arbitrary Matrices by Rangan, Sundeep, Schniter, Philip, Fletcher, Alyson K., Sarkar, Subrata

“…Approximate message passing (AMP) methods and their variants have attracted considerable recent attention for the problem of estimating a random vector x…”
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Fixed Points of Generalized Approximate Message Passing With Arbitrary Matrices by Rangan, Sundeep, Schniter, Philip, Riegler, Erwin, Fletcher, Alyson K., Cevher, Volkan

“…The estimation of a random vector with independent components passed through a linear transform followed by a componentwise (possibly nonlinear) output map…”
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Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization by Rangan, Sundeep, Fletcher, Alyson K., Schniter, Philip, Kamilov, Ulugbek S.

“…Generalized linear models, where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform z = Ax, arise in a range of…”
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Inference With Deep Generative Priors in High Dimensions by Pandit, Parthe, Sahraee-Ardakan, Mojtaba, Rangan, Sundeep, Schniter, Philip, Fletcher, Alyson K.

“…Deep generative priors offer powerful models for complex-structured data, such as images, audio, and text. Using these priors in inverse problems typically…”
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Iterative reconstruction of rank-one matrices in noise by Fletcher, Alyson K, Rangan, Sundeep

“…Abstract 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…”
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Necessary and Sufficient Conditions for Sparsity Pattern Recovery by Fletcher, A.K., Rangan, S., Goyal, V.K.

“…The paper considers the problem of detecting the sparsity pattern of a k -sparse vector in \BBR n from m random noisy measurements. A new necessary condition…”
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Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning by Kamilov, Ulugbek S., Rangan, Sundeep, Fletcher, Alyson K., Unser, Michael

“…We consider the estimation of an independent and identically distributed (i.i.d.) (possibly non-Gaussian) vector x ∈ R n from measurements y ∈ R m obtained by…”
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Cognitive Computational Neuroscience: A New Conference for an Emerging Discipline by Naselaris, Thomas, Bassett, Danielle S., Fletcher, Alyson K., Kording, Konrad, Kriegeskorte, Nikolaus, Nienborg, Hendrikje, Poldrack, Russell A., Shohamy, Daphna, Kay, Kendrick

“…Understanding the computational principles that underlie complex behavior is a central goal in cognitive science, artificial intelligence, and neuroscience. In…”
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Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks by Becker, Evan, Pandit, Parthe, Rangan, Sundeep, Fletcher, Alyson K.

“…Generative Adversarial Networks (GANs) are a popular formulation to train generative models for complex high dimensional data. The standard method for training…”
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Orthogonal Matching Pursuit: A Brownian Motion Analysis by Fletcher, A. K., Rangan, S.

“…A well-known analysis of Tropp and Gilbert shows that orthogonal matching pursuit (OMP) can recover a k -sparse n -dimensional real vector from m =4 k log( n )…”
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Generalized Autoregressive Linear Models for Discrete High-Dimensional Data by Pandit, Parthe, Sahraee-Ardakan, Mojtaba, Amini, Arash A., Rangan, Sundeep, Fletcher, Alyson K.

“…Fitting multivariate autoregressive (AR) models is fundamental for time-series data analysis in a wide range of applications. This article considers the…”
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Ranked Sparse Signal Support Detection by Fletcher, A. K., Rangan, S., Goyal, V. K.

“…This paper considers the problem of detecting the support (sparsity pattern) of a sparse vector from random noisy measurements. Conditional power of a…”
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Robust Predictive Quantization: Analysis and Design Via Convex Optimization by Fletcher, A.K., Rangan, S., Goyal, V.K., Ramchandran, K.

“…Predictive quantization is a simple and effective method for encoding slowly-varying signals that is widely used in speech and audio coding. It has been known…”
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Denoising by Sparse Approximation: Error Bounds Based on Rate-Distortion Theory by Fletcher, Alyson K, Rangan, Sundeep, Goyal, Vivek K, Ramchandran, Kannan

“…If a signal x is known to have a sparse representation with respect to a frame, it can be estimated from a noise-corrupted observation y by finding the best…”
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Vector approximate message passing for the generalized linear model by Schniter, Philip, Rangan, Sundeep, Fletcher, Alyson K.

“…The generalized linear model (GLM), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform output z = Ax,…”
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Iterative estimation of constrained rank-one matrices in noise by Rangan, S., Fletcher, A. K.

“…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…”
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On the Rate-Distortion Performance of Compressed Sensing by Fletcher, A. K., Rangan, S., Goyal, V. K.

“…Encouraging recent results in compressed sensing or compressive sampling suggest that a set of inner products with random measurement vectors forms a good…”
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