Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Tang, Gongguo"'
Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these convex optimization methods however is their lower resolution in the h
Externí odkaz:
http://arxiv.org/abs/2211.15361
Autor:
Lidiak, Alexander, Jameson, Casey, Qin, Zhen, Tang, Gongguo, Wakin, Michael B., Zhu, Zhihui, Gong, Zhexuan
It has been recently shown that a state generated by a one-dimensional noisy quantum computer is well approximated by a matrix product operator with a finite bond dimension independent of the number of qubits. We show that full quantum state tomograp
Externí odkaz:
http://arxiv.org/abs/2207.06397
Tensor train decomposition is widely used in machine learning and quantum physics due to its concise representation of high-dimensional tensors, overcoming the curse of dimensionality. Cross approximation-originally developed for representing a matri
Externí odkaz:
http://arxiv.org/abs/2207.04327
Spectral methods include a family of algorithms related to the eigenvectors of certain data-generated matrices. In this work, we are interested in studying the geometric landscape of the eigendecomposition problem in various spectral methods. In part
Externí odkaz:
http://arxiv.org/abs/2106.06574
Low-rank matrix recovery is a fundamental problem in signal processing and machine learning. A recent very popular approach to recovering a low-rank matrix X is to factorize it as a product of two smaller matrices, i.e., X = UV^T, and then optimize o
Externí odkaz:
http://arxiv.org/abs/2003.10981
The problem of estimating a sparse signal from low dimensional noisy observations arises in many applications, including super resolution, signal deconvolution, and radar imaging. In this paper, we consider a sparse signal model with non-stationary m
Externí odkaz:
http://arxiv.org/abs/1910.13104
The landscape of empirical risk has been widely studied in a series of machine learning problems, including low-rank matrix factorization, matrix sensing, matrix completion, and phase retrieval. In this work, we focus on the situation where the corre
Externí odkaz:
http://arxiv.org/abs/1907.05520
The (global) Lipschitz smoothness condition is crucial in establishing the convergence theory for most optimization methods. Unfortunately, most machine learning and signal processing problems are not Lipschitz smooth. This motivates us to generalize
Externí odkaz:
http://arxiv.org/abs/1904.09712
Principal Component Analysis (PCA) is one of the most important methods to handle high dimensional data. However, most of the studies on PCA aim to minimize the loss after projection, which usually measures the Euclidean distance, though in some fiel
Externí odkaz:
http://arxiv.org/abs/1903.06877
Non-stationary blind super-resolution is an extension of the traditional super-resolution problem, which deals with the problem of recovering fine details from coarse measurements. The non-stationary blind super-resolution problem appears in many app
Externí odkaz:
http://arxiv.org/abs/1902.05238