Search Results - "Baron, Dror"

Performance Limits With Additive Error Metrics in Noisy Multimeasurement Vector Problems by Zhu, Junan, Baron, Dror

“…Real-world applications such as magnetic resonance imaging with multiple coils, multiuser communication, and diffuse optical tomography often assume a linear…”
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The secrecy of compressed sensing measurements by Rachlin, Y., Baron, D.

“…Results in compressed sensing describe the feasibility of reconstructing sparse signals using a small number of linear measurements. In addition to compressing…”
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An O(N) semipredictive universal encoder via the BWT by Baron, D., Bresler, Y.

“…We provide an O(N) algorithm for a nonsequential semipredictive encoder whose pointwise redundancy with respect to any (unbounded depth) tree source is O(1)…”
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Rigorous State Evolution Analysis for Approximate Message Passing With Side Information by Liu, Hangjin, Rush, Cynthia, Baron, Dror

“…A common goal in many research areas is to reconstruct an unknown signal <inline-formula> <tex-math notation="LaTeX">\mathbf {x} </tex-math></inline-formula>…”
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Analysis of Approximate Message Passing With Non-Separable Denoisers and Markov Random Field Priors by Ma, Yanting, Rush, Cynthia, Baron, Dror

“…Approximate message passing (AMP) is a class of low-complexity, scalable algorithms for solving high-dimensional linear regression tasks where one wishes to…”
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Compressive Imaging via Approximate Message Passing With Image Denoising by Jin Tan, Yanting Ma, Baron, Dror

“…We consider compressive imaging problems, where images are reconstructed from a reduced number of linear measurements. Our objective is to improve over…”
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An Approximate Message Passing Framework for Side Information by Ma, Anna, Zhou, You, Rush, Cynthia, Baron, Dror, Needell, Deanna

“…Approximate message passing (AMP) methods have gained recent traction in sparse signal recovery. Additional information about the signal, or side information…”
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Bayesian Compressive Sensing Via Belief Propagation by Baron, D., Sarvotham, S., Baraniuk, R.G.

“…Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough…”
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Approximate Message Passing Algorithm With Universal Denoising and Gaussian Mixture Learning by Yanting Ma, Junan Zhu, Baron, Dror

“…We study compressed sensing (CS) signal reconstruction problems where an input signal is measured via matrix multiplication under additive white Gaussian…”
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Performance Limits for Noisy Multimeasurement Vector Problems by Junan Zhu, Baron, Dror, Krzakala, Florent

“…Compressed sensing (CS) demonstrates that sparse signals can be estimated from underdetermined linear systems. Distributed CS (DCS) further reduces the number…”
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A Universal Parallel Two-Pass MDL Context Tree Compression Algorithm by Krishnan, Nikhil, Baron, Dror

“…Computing problems that handle large amounts of data necessitate the use of lossless data compression for efficient storage and transmission. We present a…”
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Compressive Hyperspectral Imaging via Approximate Message Passing by Jin Tan, Yanting Ma, Rueda, Hoover, Baron, Dror, Arce, Gonzalo R.

“…We consider a compressive hyperspectral imaging reconstruction problem, where three-dimensional spatio-spectral information about a scene is sensed by a coded…”
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Group Testing With Side Information Via Generalized Approximate Message Passing by Cao, Shu-Jie, Goenka, Ritesh, Wong, Chau-Wai, Rajwade, Ajit, Baron, Dror

“…Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given…”
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Recovery From Linear Measurements With Complexity-Matching Universal Signal Estimation by Junan Zhu, Baron, Dror, Duarte, Marco F.

“…We study the compressed sensing (CS) signal estimation problem where an input signal is measured via a linear matrix multiplication under additive noise. While…”
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Wiener Filters in Gaussian Mixture Signal Estimation With \ell \infty -Norm Error by Tan, Jin, Baron, Dror, Dai, Liyi

“…Consider the estimation of a signal x ∈ R N from noisy observations r = x + z, where the input x is generated by an independent and identically distributed…”
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Two-Part Reconstruction With Noisy-Sudocodes by Yanting Ma, Baron, Dror, Needell, Deanna

“…We develop a two-part reconstruction framework for signal recovery in compressed sensing (CS), where a fast algorithm is applied to provide partial recovery in…”
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Contact Tracing Enhances the Efficiency of Covid-19 Group Testing by Goenka, Ritesh, Cao, Shu-Jie, Wong, Chau-Wai, Rajwade, Ajit, Baron, Dror

“…Group testing can save testing resources in the context of the ongoing COVID-19 pandemic. In group testing, we are given n samples, one per individual, and…”
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Mismatched Estimation in the Distance Geometry Problem by Abdelkhalek, Mahmoud, Baron, Dror, Wong, Chau-Wai

“…We investigate mismatched estimation in the context of the distance geometry problem (DGP). In the DGP, for a set of points, we are given noisy measurements of…”
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Fault Identification Via Nonparametric Belief Propagation by Bickson, Danny, Baron, Dror, Ihler, Alexander, Avissar, Harel, Dolev, Danny

“…We consider the problem of identifying a pattern of faults from a set of noisy linear measurements. Unfortunately, maximum a posteriori (MAP) probability…”
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Signal Estimation With Additive Error Metrics in Compressed Sensing by Jin Tan, Carmon, Danielle, Baron, Dror

“…Compressed sensing typically deals with the estimation of a system input from its noise-corrupted linear measurements, where the number of measurements is…”
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