Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

We examine a class of stochastic deep learning models with a tractable method to compute
information-theoretic quantities. Our contributions are three-fold:(i) We show how entropies …

Signal recovery from unitarily invariant measurements is investigated in this paper. A
message-passing algorithm is formulated on the basis of expectation propagation (EP). A …

We consider the problem of reconstructing an N-dimensional continuous vector x from P
constraints which are generated from its linear transformation under the assumption that the …

Approximate message passing (AMP) is a low-cost iterative parameter-estimation technique
for certain high-dimensional linear systems with non-Gaussian distributions. AMP only …

It has been observed that the performances of many high-dimensional estimation problems
are universal with respect to underlying sensing (or design) matrices. Specifically, matrices …

This paper proposes Bayes-optimal convolutional approximate message-passing (CAMP)
for signal recovery in compressed sensing. CAMP uses the same low-complexity matched …

We consider the problem of estimating a rank-$1 $ signal corrupted by structured rotationally
invariant noise, and address the following question:\emph {how well do inference algorithms …

Orthogonal/vector approximate message-passing (AMP) is a powerful message-passing
(MP) algorithm for signal reconstruction in compressed sensing. This paper proves the …

In two-dimensional (2D) photonic crystals (PC's), we demonstrate compact optical
waveguide isolators in which light can propagate only one way. Waveguides are composed …