3,252 research outputs found
Adaptive Importance Sampling in General Mixture Classes
In this paper, we propose an adaptive algorithm that iteratively updates both
the weights and component parameters of a mixture importance sampling density
so as to optimise the importance sampling performances, as measured by an
entropy criterion. The method is shown to be applicable to a wide class of
importance sampling densities, which includes in particular mixtures of
multivariate Student t distributions. The performances of the proposed scheme
are studied on both artificial and real examples, highlighting in particular
the benefit of a novel Rao-Blackwellisation device which can be easily
incorporated in the updating scheme.Comment: Removed misleading comment in Section
Complex aspects of gravity
This paper presents reflections on the validity of a series of mathematical
methods and technical assumptions that are encrusted in macrophysics (related
to gravitational interaction), that seem to have little or no physical
significance. It is interesting to inquire what a change can occur if one
removes some of the traditional assumptions.Comment: 10 page
Stochastic Discriminative EM
Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for
discriminative training of probabilistic generative models belonging to the
exponential family. In this work, we introduce and justify this algorithm as a
stochastic natural gradient descent method, i.e. a method which accounts for
the information geometry in the parameter space of the statistical model. We
show how this learning algorithm can be used to train probabilistic generative
models by minimizing different discriminative loss functions, such as the
negative conditional log-likelihood and the Hinge loss. The resulting models
trained by sdEM are always generative (i.e. they define a joint probability
distribution) and, in consequence, allows to deal with missing data and latent
variables in a principled way either when being learned or when making
predictions. The performance of this method is illustrated by several text
classification problems for which a multinomial naive Bayes and a latent
Dirichlet allocation based classifier are learned using different
discriminative loss functions.Comment: UAI 2014 paper + Supplementary Material. In Proceedings of the
Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI 2014),
edited by Nevin L. Zhang and Jian Tian. AUAI Pres
Towards Effective Low-bitwidth Convolutional Neural Networks
This paper tackles the problem of training a deep convolutional neural
network with both low-precision weights and low-bitwidth activations.
Optimizing a low-precision network is very challenging since the training
process can easily get trapped in a poor local minima, which results in
substantial accuracy loss. To mitigate this problem, we propose three
simple-yet-effective approaches to improve the network training. First, we
propose to use a two-stage optimization strategy to progressively find good
local minima. Specifically, we propose to first optimize a net with quantized
weights and then quantized activations. This is in contrast to the traditional
methods which optimize them simultaneously. Second, following a similar spirit
of the first method, we propose another progressive optimization approach which
progressively decreases the bit-width from high-precision to low-precision
during the course of training. Third, we adopt a novel learning scheme to
jointly train a full-precision model alongside the low-precision one. By doing
so, the full-precision model provides hints to guide the low-precision model
training. Extensive experiments on various datasets ( i.e., CIFAR-100 and
ImageNet) show the effectiveness of the proposed methods. To highlight, using
our methods to train a 4-bit precision network leads to no performance decrease
in comparison with its full-precision counterpart with standard network
architectures ( i.e., AlexNet and ResNet-50).Comment: 11 page
Photoemission spectra of many-polaron systems
The cross over from low to high carrier densities in a many-polaron system is
studied in the framework of the one-dimensional spinless Holstein model, using
unbiased numerical methods. Combining a novel quantum Monte Carlo approach and
exact diagonalization, accurate results for the single-particle spectrum and
the electronic kinetic energy on fairly large systems are obtained. A detailed
investigation of the quality of the Monte Carlo data is presented. In the
physically most important adiabatic intermediate electron-phonon coupling
regime, for which no analytical results are available, we observe a
dissociation of polarons with increasing band filling, leading to normal
metallic behavior, while for parameters favoring small polarons, no such
density-driven changes occur. The present work points towards the inadequacy of
single-polaron theories for a number of polaronic materials such as the
manganites.Comment: 15 pages, 13 figures; final version, accepted for publication in
Phys. Rev.
On the Modelling of an Agent's Epistemic State and its Dynamic Changes
Given a set of unquantified conditionals considered as default rules
or a set of quantified conditionals such as probabilistic rules, an
agent can build up its internal epistemic state from such a knowledge
base by inductive reasoning techniques. Besides certain (logical) knowledge,
epistemic states are supposed to allow the representation of preferences,
beliefs, assumptions etc. of an intelligent agent. If the agent lives in
a dynamic environment, it has to adapt its epistemic state constantly to
changes in the surrounding world in order to be able to react adequately
to new demands. In this paper, we present a high-level specification of
the Condor system that provides powerful methods and tools for managing
knowledge represented by conditionals and the corresponding epistemic
states of an agent. Thereby, we are able to elaborate and formalize
crucial interdependencies between different aspects of knowledge
representation, knowledge discovery, and belief revision. Moreover,
this specification, using Gurevich's Abstract State Machines, provides
the basis for a stepwise refinement development process of the Condor
system based on the ASM methodology
A semi-direct solver for compressible 3-dimensional rotational flow
An iterative procedure is presented for solving steady inviscid 3-D subsonic rotational flow problems. The procedure combines concepts from classical secondary flow theory with an extension to 3-D of a novel semi-direct Cauchy-Riemann solver. It is developed for generalized coordinates and can be exercised using standard finite difference procedures. The stability criterion of the iterative procedure is discussed along with its ability to capture the evolution of inviscid secondary flow in a turning channel
- …