Fetching the paper…
Reading the bibliography…
We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector $\theta_0\in\mathbb{R}^d$ \textit{uniformly} via $m$ quantized noisy measurements.
The spiked matrix model with generative priors
Aubin, B · 1905
Earlier work this paper cites.
Global guarantees for blind demodulation with generative priors
Hand, P · 1905
Earlier work this paper cites.
Information-theoretic lower bounds for compressive sensing with generative models
Liu, Z · 1908
Earlier work this paper cites.
Lower bounds for compressed sensing with generative models
Kamath, A · 1912
Earlier work this paper cites.
Partitions of n-space by hyperplanes
Winder, R · 1966
Earlier work this paper cites.
Fast probabilistic algorithms for Hamiltonian circuits and matchings
Angluin, D · 1979
Earlier work this paper cites.
Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach
Plan, Y · 2013
Earlier work this paper cites.
Weak convergence and empirical processes: with applications to statistics
Wellner, J · 2013
Earlier work this paper cites.
One-bit compressed sensing with non-gaussian measurements
Ai, A · 2014
Earlier work this paper cites.
Dimension reduction by random hyperplane tessellations
Plan, Y · 2014
Earlier work this paper cites.
Efficient algorithms for robust one-bit compressive sensing
Zhang, L · 2014
Earlier work this paper cites.
Why are deep nets reversible: A simple theory, with implications for training
Arora, S · 2015
Cited alongside, same era.
Towards a lower sample complexity for robust one-bit compressed sensing
Zhu, R · 2015
Cited alongside, same era.
High-dimensional estimation with geometric constraints
Plan, Y · 2016
Cited alongside, same era.
Amortised map inference for image super-resolution
Sønderby, C. K · 2016
Cited alongside, same era.
Compressed sensing using generative models
Bora, A · 2017
Cited alongside, same era.
Learning a variational network for reconstruction of accelerated mri data
Hammernik, K · 2018
Later among the works it cites.
Phase retrieval under a generative prior
Hand, P · 2018
Later among the works it cites.
Global guarantees for enforcing deep generative priors by empirical risk
Hand, P · 2018
Later among the works it cites.
A provably convergent scheme for compressive sensing under random generative priors
Huang, W · 2018
Later among the works it cites.
Geometric understanding of deep learning
Lei, N · 2018
Later among the works it cites.
The generalized lasso for sub-gaussian measurements with dithered quantization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gilbert, A. C · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C · 2017
Cited alongside, same era.
Multi-layer generalized linear estimation
Manoel, A · 2017
Cited alongside, same era.
Semantic image inpainting with deep generative models
Yeh, R. A · 2017
Cited alongside, same era.
Structured signal recovery from non-linear and heavy-tailed measurements
Goldstein, L · 2018
Cited alongside, same era.
Non-Gaussian observations in nonlinear compressed sensing via Stein discrepancies
Goldstein, L · 2018
Cited alongside, same era.
Non-gaussian hyperplane tessellations and robust one-bit compressed sensing
Dirksen, S
Cited in the paper.
Thrampoulidis, C · 2018
Later among the works it cites.
Quantized compressive sensing with rip matrices: The benefit of dithering
Xu, C · 2018
Later among the works it cites.
Dagan: Deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction
Yang, G · 2018
Later among the works it cites.
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data
Wei, X · 2019
Closest in time.
Inference with deep generative priors in high dimensions
Pandit, P · 2020
Closest in time.
Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors
Jacques, L · 2082
Closest in time.