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CEC
2011
IEEE
14 years 24 days ago
Stochastic Natural Gradient Descent by estimation of empirical covariances
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
Luigi Malagò, Matteo Matteucci, Giovanni Pi...
96
Voted
ICML
2008
IEEE
16 years 1 months ago
No-regret learning in convex games
Quite a bit is known about minimizing different kinds of regret in experts problems, and how these regret types relate to types of equilibria in the multiagent setting of repeated...
Geoffrey J. Gordon, Amy R. Greenwald, Casey Marks
ARITH
2005
IEEE
15 years 6 months ago
Fast Modular Reduction for Large Wordlengths via One Linear and One Cyclic Convolution
Abstract— Modular reduction is a fundamental operation in cryptographic systems. Most well known modular reduction methods including Barrett’s and Montgomery’s algorithms lev...
Dhananjay S. Phatak, Tom Goff
116
Voted
SIGECOM
2008
ACM
155views ECommerce» more  SIGECOM 2008»
15 years 22 days ago
Tight information-theoretic lower bounds for welfare maximization in combinatorial auctions
We provide tight information-theoretic lower bounds for the welfare maximization problem in combinatorial auctions. In this problem, the goal is to partition m items among k bidde...
Vahab S. Mirrokni, Michael Schapira, Jan Vondr&aac...
130
Voted
ICIP
2008
IEEE
16 years 2 months ago
Conditional iterative decoding of Two Dimensional Hidden Markov Models
Two Dimensional Hidden Markov Models (2D-HMMs) provide substantial benefits for many computer vision and image analysis applications. Many fundamental image analysis problems, inc...
Mehmet Emre Sargin, Alphan Altinok, Kenneth Rose, ...