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Found 13 papers, 1 papers with code
Non-uniform Array and Frequency Spacing for Regularization-free Gridless DOA
The DOA is retrieved using a Vandermonde decomposition on the Toeplitz matrix obtained from the solution of the SDP.
Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery
We first delve into the TT factorization problem and establish the local linear convergence of RGD.
Gridless DOA Estimation with Multiple Frequencies
Direction-of-arrival (DOA) estimation is widely applied in acoustic source localization.
Error Analysis of Tensor-Train Cross Approximation
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.
On Grid Compressive Sampling for Spherical Field Measurements in Acoustics
We derive a compressive sampling method for acoustic field reconstruction using field measurements on a predefined spherical grid that has theoretically guaranteed relations between signal sparsity, measurement number, and reconstruction accuracy.
Compressive Sensing with Wigner $D$-functions on Subsets of the Sphere
In this paper, we prove a compressive sensing guarantee for restricted measurement domains on the rotation group, $\mathrm{SO}(3)$.
Digital Beamforming Robust to Time-Varying Carrier Frequency Offset
We present novel beamforming algorithms that are robust to signal corruptions arising from this time-variant carrier frequency offset.
Distributed Low-rank Matrix Factorization With Exact Consensus
Low-rank matrix factorization is a problem of broad importance, owing to the ubiquity of low-rank models in machine learning contexts.
The Effectiveness of Variational Autoencoders for Active Learning
The high cost of acquiring labels is one of the main challenges in deploying supervised machine learning algorithms.
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
We also apply the theory to matrix sensing and phase retrieval to demonstrate how to infer the landscape of empirical risk from that of the corresponding population risk.
Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems
Therefore, this work not only develops guaranteed optimization methods for non-Lipschitz smooth problems but also solves an open problem of showing the second-order convergence guarantees for these alternating minimization methods.
Global Optimality in Distributed Low-rank Matrix Factorization
We study the convergence of a variant of distributed gradient descent (DGD) on a distributed low-rank matrix approximation problem wherein some optimization variables are used for consensus (as in classical DGD) and some optimization variables appear only locally at a single node in the network.
The Global Optimization Geometry of Shallow Linear Neural Networks
We examine the squared error loss landscape of shallow linear neural networks.
