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Found 6 papers, 2 papers with code
Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise
We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as looks, affected by speckle (multiplicative) noise.
GAP-net for Snapshot Compressive Imaging
The hardware encoder typically consists of an (optical) imaging system designed to capture {compressed measurements}.
Efficient Deep Approximation of GMMs
The universal approximation theorem states that any regular function can be approximated closely using a single hidden layer neural network.
Auto-encoders for compressed sensing
Compressed sensing is about recovering a structured high-dimensional signal ${\bf x}\in R^n$ from its under-determined noisy linear measurements ${\bf y}\in R^m$, where $m\ll n$.
Efficient Deep Learning of GMMs
We show that a collection of Gaussian mixture models (GMMs) in $R^{n}$ can be optimally classified using $O(n)$ neurons in a neural network with two hidden layers (deep neural network), whereas in contrast, a neural network with a single hidden layer (shallow neural network) would require at least $O(\exp(n))$ neurons or possibly exponentially large coefficients.
Linear Time Clustering for High Dimensional Mixtures of Gaussian Clouds
Consequently, the expected overall running time of the algorithm is linear in $n$ and quasi-linear in $p$ at $o(\ln{p})O(np)$, and the sample complexity is independent of $p$.
