This paper proposes a novel maximum entropy based rule selection (MERS) model for syntax-based statistical machine translation (SMT). The MERS model combines local contextual info...
We propose in this paper to unify two different ap-
proaches to image restoration: On the one hand, learning a
basis set (dictionary) adapted to sparse signal descriptions
has p...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed,...
In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the ...
DOALL loops are tiled to exploit DOALL parallelism and data locality on GPUs. In contrast, due to loop-carried dependences, DOACROSS loops must be skewed first in order to make ti...