Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Neumann networks for linear inverse problems in imaging
Many challenging image processing tasks can be described by an ill-posed linear inverse
problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all …
problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all …
Sketching for large-scale learning of mixture models
Learning parameters from voluminous data can be prohibitive in terms of memory and
computational requirements. We propose a 'compressive learning'framework, where we …
computational requirements. We propose a 'compressive learning'framework, where we …
Multilinear compressive learning
Compressive learning (CL) is an emerging topic that combines signal acquisition via
compressive sensing (CS) and machine learning to perform inference tasks directly on a …
compressive sensing (CS) and machine learning to perform inference tasks directly on a …
Classification and reconstruction of high-dimensional signals from low-dimensional features in the presence of side information
This paper offers a characterization of fundamental limits on the classification and
reconstruction of high-dimensional signals from low-dimensional features, in the presence of …
reconstruction of high-dimensional signals from low-dimensional features, in the presence of …
Reconstruction of signals drawn from a Gaussian mixture via noisy compressive measurements
This paper determines to within a single measurement the minimum number of
measurements required to successfully reconstruct a signal drawn from a Gaussian mixture …
measurements required to successfully reconstruct a signal drawn from a Gaussian mixture …
Bounds on the number of measurements for reliable compressive classification
This paper studies the classification of high-dimensional Gaussian signals from low-
dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient …
dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient …
Compressive detection of random subspace signals
The problem of compressive detection of random subspace signals is studied. We consider
signals modeled as s= Hx where H is an N× K matrix with K≤ N and x~ N (0 K, 1, σ∞ 2 IK) …
signals modeled as s= Hx where H is an N× K matrix with K≤ N and x~ N (0 K, 1, σ∞ 2 IK) …
Selective CS: An energy-efficient sensing architecture for wireless implantable neural decoding
The spike classification is a critical step in the implantable neural decoding. The energy
efficiency issue in the sensor node is a big challenge for the entire system. Compressive …
efficiency issue in the sensor node is a big challenge for the entire system. Compressive …
Performance bounds of compressive classification under perturbation
Recently, compressive sensing based classification, which is called compressive
classification, has drawn a lot of attention, since it works directly in the compressive domain …
classification, has drawn a lot of attention, since it works directly in the compressive domain …
Projections designs for compressive classification
This paper puts forth projections designs for compressive classification of Gaussian mixture
models. In particular, we capitalize on the asymptotic characterization of the behavior of an …
models. In particular, we capitalize on the asymptotic characterization of the behavior of an …