6,105 research outputs found
Comparison theorem of one-dimensional stochastic hybrid delay systems
The comparison theorem of stochastic differential equations has been investigated by many authors. However, little research is available on the comparison theorem of stochastic hybrid systems, which is the topic of this paper. The systems discussed is stochastic delay differential equations with Markovian switching. It is an important class of hybrid systems
Improved Dropout for Shallow and Deep Learning
Dropout has been witnessed with great success in training deep neural
networks by independently zeroing out the outputs of neurons at random. It has
also received a surge of interest for shallow learning, e.g., logistic
regression. However, the independent sampling for dropout could be suboptimal
for the sake of convergence. In this paper, we propose to use multinomial
sampling for dropout, i.e., sampling features or neurons according to a
multinomial distribution with different probabilities for different
features/neurons. To exhibit the optimal dropout probabilities, we analyze the
shallow learning with multinomial dropout and establish the risk bound for
stochastic optimization. By minimizing a sampling dependent factor in the risk
bound, we obtain a distribution-dependent dropout with sampling probabilities
dependent on the second order statistics of the data distribution. To tackle
the issue of evolving distribution of neurons in deep learning, we propose an
efficient adaptive dropout (named \textbf{evolutional dropout}) that computes
the sampling probabilities on-the-fly from a mini-batch of examples. Empirical
studies on several benchmark datasets demonstrate that the proposed dropouts
achieve not only much faster convergence and but also a smaller testing error
than the standard dropout. For example, on the CIFAR-100 data, the evolutional
dropout achieves relative improvements over 10\% on the prediction performance
and over 50\% on the convergence speed compared to the standard dropout.Comment: In NIPS 201
Analytical Solutions of Singular Isothermal Quadrupole Lens
Using analytical method, we study the Singular Isothermal Quadrupole (SIQ)
lens system, which is the simplest lens model that can produce four images. In
this case, the radial mass distribution is in accord with the profile of the
Singular Isothermal Sphere (SIS) lens, and the tangential distribution is given
by adding a quadrupole on the monopole component. The basic properties of the
SIQ lens have been studied in this paper, including deflection potential,
deflection angle, magnification, critical curve, caustic, pseudo-caustic and
transition locus. Analytical solutions of the image positions and
magnifications for the source on axes are derived. As have been found, naked
cusps will appear when the relative intensity of quadrupole to monopole is
larger than 0.6. According to the magnification invariant theory of the SIQ
lens, the sum of the signed magnifications of the four images should be equal
to unity \citep{dal98}. However, if a source lies in the naked cusp, the summed
magnification of the left three images is smaller than the invariant 1. With
this simple lens system, we study the situations that a point source infinitely
approaches a cusp or a fold. The sum of magnifications of cusp image triplet is
usually not equal to 0, and it is usually positive for major cusp while
negative for minor cusp. Similarly, the sum of magnifications of fold image
pair is usually neither equal to 0. Nevertheless, the cusp and fold relations
are still equal to 0, in that the sum values are divided by infinite absolute
magnifications by definition.Comment: 12 pages, 2 figures, accepted for publication in ApJ
Searching for a preferred direction with Union2.1 data
A cosmological preferred direction was reported from the type Ia supernovae
(SNe Ia) data in recent years. We use the Union2.1 data to give a simple
classification of such studies for the first time. Because the maximum
anisotropic direction is independent of isotropic dark energy models, we adopt
two cosmological models (CDM, CDM) for the hemisphere comparison
analysis and CDM model for dipole fit approach. In hemisphere
comparison method, the matter density and the equation of state of dark energy
are adopted as the diagnostic qualities in the CDM model and CDM
model, respectively. In dipole fit approach, we fit the fluctuation of distance
modulus. We find that there is a null signal for the hemisphere comparison
method, while a preferred direction () for the dipole fit method. This result indicates
that the dipole fit is more sensitive than the hemisphere comparison method.Comment: 8 pages, 2 figures, accepted for publication in MNRA
Anticipated backward stochastic differential equations
In this paper we discuss new types of differential equations which we call
anticipated backward stochastic differential equations (anticipated BSDEs). In
these equations the generator includes not only the values of solutions of the
present but also the future. We show that these anticipated BSDEs have unique
solutions, a comparison theorem for their solutions, and a duality between them
and stochastic differential delay equations.Comment: Published in at http://dx.doi.org/10.1214/08-AOP423 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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