We analyze the performance of the least absolute shrinkage and selection...
In realistic compressed sensing (CS) scenarios, the obtained measurement...
We consider the ubiquitous linear inverse problems with additive Gaussia...
We consider the general problem of recovering a high-dimensional signal ...
We consider the problem of recovering an unknown signal 𝐱∈ℝ^n from gener...
This work finds the exact solutions to a deep linear network with weight...
This work theoretically studies stochastic neural networks, a main type ...
We consider the problem of high-dimensional Ising model selection using
...
We theoretically investigate the performance of ℓ_1-regularized linear
r...
Inferring interaction parameters from observed data is a ubiquitous
requ...
Restricted Boltzmann machines (RBMs) with low-precision synapses are muc...
Neural networks with binary weights are computation-efficient and
hardwa...
Non-orthogonal multiple access (NoMA) as an efficient way of radio resou...
Given its capability in efficient radio resource sharing, non-orthogonal...
In this paper, the line spectral estimation (LSE) problem is studied fro...
This paper considers the generalized bilinear recovery problem which aim...
In future wireless networks, one fundamental challenge for massive
machi...
In this letter, a binary sparse Bayesian learning (BSBL) algorithm is
pr...
In this letter, we present a unified Bayesian inference framework for
ge...