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NIPS
2008
13 years 11 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
LICS
2003
IEEE
14 years 3 months ago
Labelled Markov Processes: Stronger and Faster Approximations
This paper reports on and discusses three notions of approximation for Labelled Markov Processes that have been developed last year. The three schemes are improvements over former...
Vincent Danos, Josee Desharnais
SEMWEB
2007
Springer
14 years 4 months ago
Change Paths in Reasoning!
Millions of research funding has been put down to develop - what I call - old forms - of reasoning that are characterized by strong focus on theoretical properties and strict adher...
Raphael Volz
ICASSP
2011
IEEE
13 years 1 months ago
SRF: Matrix completion based on smoothed rank function
In this paper, we address the matrix completion problem and propose a novel algorithm based on a smoothed rank function (SRF) approximation. Among available algorithms like FPCA a...
Hooshang Ghasemi, Mohmmadreza Malek-Mohammadi, Mas...
WIOPT
2011
IEEE
13 years 1 months ago
Lower bounds on the success probability for ad hoc networks with local FDMA scheduling
—This paper studies the performance of ad hoc networks with local FDMA scheduling using stochastic point processes. In such networks, the Poisson assumption is not justified due...
Ralph Tanbourgi, Jens P. Elsner, Holger Jäkel...