7,818 research outputs found
Shakeout: A New Approach to Regularized Deep Neural Network Training
Recent years have witnessed the success of deep neural networks in dealing
with a plenty of practical problems. Dropout has played an essential role in
many successful deep neural networks, by inducing regularization in the model
training. In this paper, we present a new regularized training approach:
Shakeout. Instead of randomly discarding units as Dropout does at the training
stage, Shakeout randomly chooses to enhance or reverse each unit's contribution
to the next layer. This minor modification of Dropout has the statistical
trait: the regularizer induced by Shakeout adaptively combines , and
regularization terms. Our classification experiments with representative
deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that
Shakeout deals with over-fitting effectively and outperforms Dropout. We
empirically demonstrate that Shakeout leads to sparser weights under both
unsupervised and supervised settings. Shakeout also leads to the grouping
effect of the input units in a layer. Considering the weights in reflecting the
importance of connections, Shakeout is superior to Dropout, which is valuable
for the deep model compression. Moreover, we demonstrate that Shakeout can
effectively reduce the instability of the training process of the deep
architecture.Comment: Appears at T-PAMI 201
transitions in the light cone sum rules with the chiral current
semi-leptonic decays to the light scalar meson, , are investigated in the QCD
light-cone sum rules (LCSR) with chiral current correlator. Having little
knowledge of ingredients of the scalar mesons, we confine ourself to the two
quark picture for them and work with the two possible Scenarios. The resulting
sum rules for the form factors receive no contributions from the twist-3
distribution amplitudes (DA's), in comparison with the calculation of the
conventional LCSR approach where the twist-3 parts play usually an important
role. We specify the range of the squared momentum transfer , in which the
operator product expansion (OPE) for the correlators remains valid
approximately. It is found that the form factors satisfy a relation consistent
with the prediction of soft collinear effective theory (SCET). In the effective
range we investigate behaviors of the form factors and differential decay
widthes and compare our calculations with the observations from other
approaches. The present findings can be beneficial to experimentally identify
physical properties of the scalar mesons.Comment: 22 pages,16 figure
Genetic transformation of Torenia fournieri L. mediated by Agrobacterium rhizogenes
AbstractThe transformation of Torenia fournieri L. mediated by Agrobacterium rhizogenes was studied. Almost all roots induced by four bacterial strains, R1000, R1601, A4 and R1205 were putative hairy roots. The effects of bacterial strains, bacterial concentration, acetosyringone, silver nitrate and co-cultivation pH on Torenia transformation were investigated. Strain R1000, co-cultivation for 3 days, 30 μmol L−1 acetosyringone, 4 mg L−1 silver nitrate and pH 6.5 in the cultivation medium provided the optimal conditions under which transformation frequency approached 90%
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