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
Nonconvex optimization meets low-rank matrix factorization: An overview
Substantial progress has been made recently on develo** provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …
[کتاب][B] Random matrix methods for machine learning
R Couillet, Z Liao - 2022 - books.google.com
This book presents a unified theory of random matrices for applications in machine learning,
offering a large-dimensional data vision that exploits concentration and universality …
offering a large-dimensional data vision that exploits concentration and universality …
Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion
Recent years have seen a flurry of activities in designing provably efficient nonconvex
optimization procedures for solving statistical estimation problems. For various problems like …
optimization procedures for solving statistical estimation problems. For various problems like …
Gradient descent with random initialization: Fast global convergence for nonconvex phase retrieval
This paper considers the problem of solving systems of quadratic equations, namely,
recovering an object of interest x^ ♮ ∈ R^ nx♮∈ R n from m quadratic equations/samples …
recovering an object of interest x^ ♮ ∈ R^ nx♮∈ R n from m quadratic equations/samples …
Phasemax: Convex phase retrieval via basis pursuit
We consider the recovery of a (real-or complex-valued) signal from magnitude-only
measurements, known as phase retrieval. We formulate phase retrieval as a convex …
measurements, known as phase retrieval. We formulate phase retrieval as a convex …
The numerics of phase retrieval
Phase retrieval, ie the problem of recovering a function from the squared magnitude of its
Fourier transform, arises in many applications, such as X-ray crystallography, diffraction …
Fourier transform, arises in many applications, such as X-ray crystallography, diffraction …
Online stochastic gradient descent on non-convex losses from high-dimensional inference
Stochastic gradient descent (SGD) is a popular algorithm for optimization problems arising
in high-dimensional inference tasks. Here one produces an estimator of an unknown …
in high-dimensional inference tasks. Here one produces an estimator of an unknown …
Fundamental limits of weak recovery with applications to phase retrieval
In phase retrieval we want to recover an unknown signal $\boldsymbol x\in\mathbb C^ d $
from $ n $ quadratic measurements of the form $ y_i=|⟨\boldsymbol a_i,\boldsymbol x⟩|^ 2+ …
from $ n $ quadratic measurements of the form $ y_i=|⟨\boldsymbol a_i,\boldsymbol x⟩|^ 2+ …
Estimation in rotationally invariant generalized linear models via approximate message passing
We consider the problem of signal estimation in generalized linear models defined via
rotationally invariant design matrices. Since these matrices can have an arbitrary spectral …
rotationally invariant design matrices. Since these matrices can have an arbitrary spectral …