23,428 research outputs found
Compression of Deep Neural Networks on the Fly
Thanks to their state-of-the-art performance, deep neural networks are
increasingly used for object recognition. To achieve these results, they use
millions of parameters to be trained. However, when targeting embedded
applications the size of these models becomes problematic. As a consequence,
their usage on smartphones or other resource limited devices is prohibited. In
this paper we introduce a novel compression method for deep neural networks
that is performed during the learning phase. It consists in adding an extra
regularization term to the cost function of fully-connected layers. We combine
this method with Product Quantization (PQ) of the trained weights for higher
savings in storage consumption. We evaluate our method on two data sets (MNIST
and CIFAR10), on which we achieve significantly larger compression rates than
state-of-the-art methods
Efficient Scheme for Perfect Collective Einstein-Podolsky-Rosen Steering
A practical scheme for the demonstration of perfect one-sided
device-independent quantum secret sharing is proposed. The scheme involves a
three-mode optomechanical system in which a pair of independent cavity modes is
driven by short laser pulses and interact with a movable mirror. We demonstrate
that by tuning the laser frequency to the blue (anti-Stokes) sideband of the
average frequency of the cavity modes, the modes become mutually coherent and
then may collectively steer the mirror mode to a perfect
Einstein-Podolsky-Rosen state. The scheme is shown to be experimentally
feasible, it is robust against the frequency difference between the modes,
mechanical thermal noise and damping, and coupling strengths of the cavity
modes to the mirror.Comment: 9 pages, 4 figure
Aspects of Floquet Bands and Topological Phase Transitions in a Continuously Driven Superlattice
Recently the creation of novel topological states of matter by a periodic
driving field has attracted great attention. To motivate further experimental
and theoretical studies, we investigate interesting aspects of Floquet bands
and topological phase transitions in a continuously driven Harper model. In
such a continuously driven system with an odd number of Floquet bands, the
bands are found to have nonzero Chern numbers in general and topological phase
transitions take place as we tune various system parameters, such as the
amplitude or the period of the driving field. The nontrivial Floquet band
topology results in a quantized transport of Wannier states in the lattice
space. For certain parameter choices, very flat yet topologically nontrivial
Floquet bands may also emerge, a feature that is potentially useful for the
simulation of physics of strongly correlated systems. Some cases with an even
number of Floquet bands may also have intriguing Dirac cones in the spectrum.
Under open boundary conditions, anomalous counter-propagating chiral edge modes
and degenerate zero modes are also found as the system parameters are tuned.
These results should be of experimental interest because a continuously driven
system is easier to realize than a periodically kicked system.Comment: 29 pages, 9 figures. Comments are welcom
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