4 accepted papers
This paper investigates distributed estimation problems with factorized structures over factor graphs. By building upon the recent progress in the approximate message passing (AMP) paradigm, this paper extends the vector AMP (VAMP) algorithm to the distributed scenario where multiple agents collabor…
Approximate message passing (AMP) algorithms are devised under the Gaussianity assumption of the measurement noise vector. In this work, we relax this assumption within the vector AMP (VAMP) framework to arbitrary independent and identically distributed (i.i.d.) noise priors. We do so by rederiving…
This paper introduces a new algorithmic solution to the massive unsourced random access (mURA) problem. The proposed uncoupled compressed sensing (UCS)-based scheme relies on slotted transmissions and takes advantage of the inherent coupling provided by the users’ spatial signatures in the form of c…
Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning w…