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Found 11 papers, 2 papers with code
Dictionary Learning under Symmetries via Group Representations
We apply our paradigm to investigate the dictionary learning problem for the groups SO(2) and SO(3).
Generative Principal Component Analysis
We perform experiments on various image datasets for spiked matrix and phase retrieval models, and illustrate performance gains of our method to the classic power method and the truncated power method devised for sparse principal component analysis.
Learning with latent group sparsity via heat flow dynamics on networks
Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticians alike.
Conjugation Invariant Learning with Neural Networks
Thus, a naturally significant class of functions consists of those that are intrinsic to the problem, in the sense of being independent of such base change or relabelling; in other words invariant under the conjugation action by a group.
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors
In 1-bit compressive sensing, each measurement is quantized to a single bit, namely the sign of a linear function of an unknown vector, and the goal is to accurately recover the vector.
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
We also adapt this result to sparse phase retrieval, and show that $O(s \log n)$ samples are sufficient for a similar guarantee when the underlying signal is $s$-sparse and $n$-dimensional, matching an information-theoretic lower bound.
Signal Analysis via the Stochastic Geometry of Spectrogram Level Sets
In this work, we investigate spectrogram analysis via an examination of the stochastic geometric properties of their level sets.
On a Variational Approximation based Empirical Likelihood ABC Method
The expected log-likelihood is then estimated by an empirical likelihood where the only inputs required are a choice of summary statistic, it's observed value, and the ability to simulate the chosen summary statistics for any parameter value under the model.
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
We propose a novel stochastic network model, called Fractal Gaussian Network (FGN), that embodies well-defined and analytically tractable fractal structures.
Learning from DPPs via Sampling: Beyond HKPV and symmetry
Our approach is scalable and applies to very general DPPs, beyond traditional symmetric kernels.
Transmission and navigation on disordered lattice networks, directed spanning forests and Brownian web
Stochastic networks based on random point sets as nodes have attracted considerable interest in many applications, particularly in communication networks, including wireless sensor networks, peer-to-peer networks and so on.
