85,808 research outputs found
Crowd Counting with Decomposed Uncertainty
Research in neural networks in the field of computer vision has achieved
remarkable accuracy for point estimation. However, the uncertainty in the
estimation is rarely addressed. Uncertainty quantification accompanied by point
estimation can lead to a more informed decision, and even improve the
prediction quality. In this work, we focus on uncertainty estimation in the
domain of crowd counting. With increasing occurrences of heavily crowded events
such as political rallies, protests, concerts, etc., automated crowd analysis
is becoming an increasingly crucial task. The stakes can be very high in many
of these real-world applications. We propose a scalable neural network
framework with quantification of decomposed uncertainty using a bootstrap
ensemble. We demonstrate that the proposed uncertainty quantification method
provides additional insight to the crowd counting problem and is simple to
implement. We also show that our proposed method exhibits the state of the art
performances in many benchmark crowd counting datasets.Comment: Accepted in AAAI 2020 (Main Technical Track
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection
Long Short-Term Memory networks trained with gradient descent and
back-propagation have received great success in various applications. However,
point estimation of the weights of the networks is prone to over-fitting
problems and lacks important uncertainty information associated with the
estimation. However, exact Bayesian neural network methods are intractable and
non-applicable for real-world applications. In this study, we propose an
approximate estimation of the weights uncertainty using Ensemble Kalman Filter,
which is easily scalable to a large number of weights. Furthermore, we optimize
the covariance of the noise distribution in the ensemble update step using
maximum likelihood estimation. To assess the proposed algorithm, we apply it to
outlier detection in five real-world events retrieved from the Twitter
platform
State-space based mass event-history model I: many decision-making agents with one target
A dynamic decision-making system that includes a mass of indistinguishable
agents could manifest impressive heterogeneity. This kind of nonhomogeneity is
postulated to result from macroscopic behavioral tactics employed by almost all
involved agents. A State-Space Based (SSB) mass event-history model is
developed here to explore the potential existence of such macroscopic
behaviors. By imposing an unobserved internal state-space variable into the
system, each individual's event-history is made into a composition of a common
state duration and an individual specific time to action. With the common state
modeling of the macroscopic behavior, parametric statistical inferences are
derived under the current-status data structure and conditional independence
assumptions. Identifiability and computation related problems are also
addressed. From the dynamic perspectives of system-wise heterogeneity, this SSB
mass event-history model is shown to be very distinct from a random effect
model via the Principle Component Analysis (PCA) in a numerical experiment.
Real data showing the mass invasion by two species of parasitic nematode into
two species of host larvae are also analyzed. The analysis results not only are
found coherent in the context of the biology of the nematode as a parasite, but
also include new quantitative interpretations.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS189 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Scalable MCEM Estimator for Spatio-Temporal Autoregressive Models
Very large spatio-temporal lattice data are becoming increasingly common
across a variety of disciplines. However, estimating interdependence across
space and time in large areal datasets remains challenging, as existing
approaches are often (i) not scalable, (ii) designed for conditionally Gaussian
outcome data, or (iii) are limited to cross-sectional and univariate outcomes.
This paper proposes an MCEM estimation strategy for a family of latent-Gaussian
multivariate spatio-temporal models that addresses these issues. The proposed
estimator is applicable to a wide range of non-Gaussian outcomes, and
implementations for binary and count outcomes are discussed explicitly. The
methodology is illustrated on simulated data, as well as on weekly data of
IS-related events in Syrian districts.Comment: 29 pages, 8 figure
Likelihood inference for exponential-trawl processes
Integer-valued trawl processes are a class of serially correlated, stationary
and infinitely divisible processes that Ole E. Barndorff-Nielsen has been
working on in recent years. In this Chapter, we provide the first analysis of
likelihood inference for trawl processes by focusing on the so-called
exponential-trawl process, which is also a continuous time hidden Markov
process with countable state space. The core ideas include prediction
decomposition, filtering and smoothing, complete-data analysis and EM
algorithm. These can be easily scaled up to adapt to more general trawl
processes but with increasing computation efforts.Comment: 29 pages, 6 figures, forthcoming in: "A Fascinating Journey through
Probability, Statistics and Applications: In Honour of Ole E.
Barndorff-Nielsen's 80th Birthday", Springer, New Yor
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