Add to ORKGpeer reviewedWe propose a gossip-based distributed algorithm for Gaussian mixture learning, Newscast EM. The algorithm operates on network topologies where each node observes a local quantity and can communicate with other nodes in an arbitrary point-to-point fashion. The main difference between Newscast EM and the standard EM algorithm is that the M-step in our case is implemented in a decentralized manner: (random) pairs of nodes repeatedly exchange their local parameter estimates and combine them by (weighted) averaging. We provide theoretical evidence and demonstrate experimentally that, under this protocol, nodes converge exponentially fast to the correct estimates in each M-step of the EM algorithm
Unlike the telephone network or the Internet, many of the next generation networks are not engineere...
This paper presents a novel adaptation mechanism that allows every node of a gossip-based broadcast ...
Recently, gossip algorithms have received much attention from the wireless sensor network community ...
peer reviewedIt has been recently demonstrated that the classical EM algorithm for learning Gaussian...
International audienceThe present paper deals with pattern recognition in a distributed computing co...
Gossip is a well-known technique for distributed computing in an arbitrarily connected network, that...
We introduce a technique for accelerating the gos- sip algorithm of Boyd et. al. (INFOCOM 2005) for ...
Abstract—This paper describes a local and distributed ex-pectation maximization algorithm for learni...
Abstract—Motivated by applications to wireless sensor, peer-to-peer, and ad hoc networks, we study d...
Gossiping is a distributed process whose purpose is to enable the members of a group of n > 1 autono...
International audienceEfficient and robust algorithms for decentralized estimation in networks are e...
This paper investigates accelerated gossip algorithms for distributed computations in networks where...
International audienceEfficient and robust algorithms for decentralized estimation in networks are e...
The increasing importance of gossip algorithms is beyond dispute. Randomized gossip algorithms are a...
In this paper we investigate the limit performance of Floating Gossip, a new, fully distributed Goss...
Unlike the telephone network or the Internet, many of the next generation networks are not engineere...
This paper presents a novel adaptation mechanism that allows every node of a gossip-based broadcast ...
Recently, gossip algorithms have received much attention from the wireless sensor network community ...
peer reviewedIt has been recently demonstrated that the classical EM algorithm for learning Gaussian...
International audienceThe present paper deals with pattern recognition in a distributed computing co...
Gossip is a well-known technique for distributed computing in an arbitrarily connected network, that...
We introduce a technique for accelerating the gos- sip algorithm of Boyd et. al. (INFOCOM 2005) for ...
Abstract—This paper describes a local and distributed ex-pectation maximization algorithm for learni...
Abstract—Motivated by applications to wireless sensor, peer-to-peer, and ad hoc networks, we study d...
Gossiping is a distributed process whose purpose is to enable the members of a group of n > 1 autono...
International audienceEfficient and robust algorithms for decentralized estimation in networks are e...
This paper investigates accelerated gossip algorithms for distributed computations in networks where...
International audienceEfficient and robust algorithms for decentralized estimation in networks are e...
The increasing importance of gossip algorithms is beyond dispute. Randomized gossip algorithms are a...
In this paper we investigate the limit performance of Floating Gossip, a new, fully distributed Goss...
Unlike the telephone network or the Internet, many of the next generation networks are not engineere...
This paper presents a novel adaptation mechanism that allows every node of a gossip-based broadcast ...
Recently, gossip algorithms have received much attention from the wireless sensor network community ...