Zobrazeno 1 - 10
of 955
pro vyhledávání: '"Davenport, Mark A."'
Autor:
Guan, Peimeng, Davenport, Mark A.
Inverse problems aim to reconstruct unseen data from corrupted or perturbed measurements. While most work focuses on improving reconstruction quality, generalization accuracy and robustness are equally important, especially for safety-critical applic
Externí odkaz:
http://arxiv.org/abs/2410.14667
We introduce a new method for robust beamforming, where the goal is to estimate a signal from array samples when there is uncertainty in the angle of arrival. Our method offers state-of-the-art performance on narrowband signals and is naturally appli
Externí odkaz:
http://arxiv.org/abs/2406.16304
Model-based deep learning methods such as loop unrolling (LU) and deep equilibrium model}(DEQ) extensions offer outstanding performance in solving inverse problems (IP). These methods unroll the optimization iterations into a sequence of neural netwo
Externí odkaz:
http://arxiv.org/abs/2403.04847
In this paper we revisit the classical problem of estimating a signal as it impinges on a multi-sensor array. We focus on the case where the impinging signal's bandwidth is appreciable and is operating in a broadband regime. Estimating broadband sign
Externí odkaz:
http://arxiv.org/abs/2312.03922
We introduce a new type of query mechanism for collecting human feedback, called the perceptual adjustment query ( PAQ). Being both informative and cognitively lightweight, the PAQ adopts an inverted measurement scheme, and combines advantages from b
Externí odkaz:
http://arxiv.org/abs/2309.04626
We analyze the popular ``state-space'' class of algorithms for detecting casual interaction in coupled dynamical systems. These algorithms are often justified by Takens' embedding theorem, which provides conditions under which relationships involving
Externí odkaz:
http://arxiv.org/abs/2308.06855
Seismic deconvolution is an essential step in seismic data processing that aims to extract layer information from noisy observed traces. In general, this is an ill-posed problem with non-unique solutions. Due to the sparse nature of the reflectivity
Externí odkaz:
http://arxiv.org/abs/2307.10030
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain suf
Externí odkaz:
http://arxiv.org/abs/2305.02304
Detecting change points sequentially in a streaming setting, especially when both the mean and the variance of the signal can change, is often a challenging task. A key difficulty in this context often involves setting an appropriate detection thresh
Externí odkaz:
http://arxiv.org/abs/2210.17353
Autor:
DeLude, Coleman, Sharma, Rakshith, Karnik, Santhosh, Hood, Christopher, Davenport, Mark, Romberg, Justin
In this paper we consider the problem of localizing a set of broadband sources from a finite window of measurements. In the case of narrowband sources this can be reduced to the problem of spectral line estimation, where our goal is simply to estimat
Externí odkaz:
http://arxiv.org/abs/2210.11669