P10-1074 describe the more complicated case of stochastic optimization with a hierarchical objective
P08-1109 Function Evaluation Utilization of stochastic optimization routines requires the implementation
P08-1109 three sources . We made use of stochastic optimization methods which allow us to find
W13-2232 large aligned collections . Online stochastic optimization inference allows us to generate
D10-1081 cost function . Mechanisms for stochastic optimization implemented in the place of the
D12-1046 effective for training , such as stochastic optimization ( Finkel et al. , 2008 ) . In
N12-1084 then be combined with LDA with stochastic optimization ( Andrzejewski et al. , 2011
P14-1098 existing example ontologies using stochastic optimization , automatically learning the
W11-2139 rate training ( Och , 2003 ) is a stochastic optimization algorithm that typically finds
P10-1074 and often requires the use of stochastic optimization in order for the optimization
P08-1109 is primarily due to the use of stochastic optimization techniques , as well as parallelization
P88-1013 using simulated annealing , a stochastic optimization technique . Beginning with an
P13-2030 be interesting to investigate stochastic optimization techniques for regularized compression
P08-1109 set of trees . Because we use a stochastic optimization method , as discussed in Section
P05-1069 been widely used in complicated stochastic optimization problems such as neural networks
P98-2219 dialogue strategy can be treated as a stochastic optimization problem ( Walker , 1993 ; Biermann
P08-1109 function . Instead of using a stochastic optimization technique , they use L - BFGS
J95-2001 the model parameters ) . Three stochastic optimization criteria and seven European languages
P13-2108 we fix weights using parallel stochastic optimization of a structured SVM objective
W12-1703 Jordan , 2000 ) to transform this stochastic optimization problem into a deterministic
hide detail