6 accepted papers
The Lookahead optimizer [Zhang et al., 2019] was recently proposed and demonstrated to improve performance of stochastic first-order methods for training deep neural networks. Lookahead can be viewed as a two time-scale algorithm, where the fast dynamics (inner optimizer) determine a search directio…
In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iterati…
Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the g…
We consider the problem of inferring a graph from signals which are assumed to be smooth over the graph, in the setting where the graph is also assumed to be sparse. We focus on the case where measurements are Gaussian vectors and the graph topology is encoded in the inverse of the covariance matrix…
We present a distributed cardinalized probability hypothesis density (CPHD) filter for multi-sensor multi-target tracking. Each sensor runs a single-sensor CPHD filter to compute the probability hypothesis density (PHD) function and cardinality distribution using only its own measurements and then f…
Random finite set (RFS) based filters such as the cardinalized probability hypothesis density (CPHD) filter have been successfully applied to the problem of single sensor multitarget tracking. Various multisensor extensions of these filters have been proposed in the literature, but exact update equa…