432 research outputs found
Time course of information processing in visual and haptic object classification
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Bayesian alignments of warped multi-output Gaussian processes
We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field. The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes. We present an efficient varia-tional approximation based on nested variational compression and show how the model can be used to extract shared information between dependent time series, recovering an interpretable functional decomposition of the learning problem. We show results for an artificial data set and real-world data of two wind turbines
Entrepreneurship and Resource Allocation
Historical surveys show that most analyses of the entrepreneur focus primarily upon one aspect or function of entrepreneurship such as risk-bearing, innovation, or the perception of and response to market disequilibria. Despite the richness of entrepreneurial theory over the last two hundred years, economists have yet to adequately model the market for entrepreneurship. The analysis in this paper implies a supply of entrepreneurial expenditures function, that when coupled with a concept of the demand for entrepreneurial expenditures, yields a market model of the entrepreneurial process. The model is significant in three important ways; first, it synthesizes the different but complementary elements of entrepreneurial behavior that have been separately developed in the received literature, second, it explains how market forces drive an economy toward an efficient allocation of entrepreneurial resources, and third, it provides a framework for considering and analyzing policy actions to stimulate entrepreneurial activity and economic growth.
A locally time-invariant metric for climate model ensemble predictions of extreme risk
Adaptation-relevant predictions of climate change are often derived by
combining climate model simulations in a multi-model ensemble. Model evaluation
methods used in performance-based ensemble weighting schemes have limitations
in the context of high-impact extreme events. We introduce a locally
time-invariant method for evaluating climate model simulations with a focus on
assessing the simulation of extremes. We explore the behaviour of the proposed
method in predicting extreme heat days in Nairobi and provide comparative
results for eight additional cities
Nonminimal Couplings in the Early Universe: Multifield Models of Inflation and the Latest Observations
Models of cosmic inflation suggest that our universe underwent an early phase
of accelerated expansion, driven by the dynamics of one or more scalar fields.
Inflationary models make specific, quantitative predictions for several
observable quantities, including particular patterns of temperature anistropies
in the cosmic microwave background radiation. Realistic models of high-energy
physics include many scalar fields at high energies. Moreover, we may expect
these fields to have nonminimal couplings to the spacetime curvature. Such
couplings are quite generic, arising as renormalization counterterms when
quantizing scalar fields in curved spacetime. In this chapter I review recent
research on a general class of multifield inflationary models with nonminimal
couplings. Models in this class exhibit a strong attractor behavior: across a
wide range of couplings and initial conditions, the fields evolve along a
single-field trajectory for most of inflation. Across large regions of phase
space and parameter space, therefore, models in this general class yield robust
predictions for observable quantities that fall squarely within the "sweet
spot" of recent observations.Comment: 17pp, 2 figs. References added to match the published version.
Published in {\it At the Frontier of Spacetime: Scalar-Tensor Theory, Bell's
Inequality, Mach's Principle, Exotic Smoothness}, ed. T. Asselmeyer-Maluga
(Springer, 2016), pp. 41-57, in honor of Carl Brans's 80th birthda
Compositional Uncertainty in Deep Gaussian Processes
Gaussian processes (GPs) are nonparametric priors over functions. Fitting a
GP implies computing a posterior distribution of functions consistent with the
observed data. Similarly, deep Gaussian processes (DGPs) should allow us to
compute a posterior distribution of compositions of multiple functions giving
rise to the observations. However, exact Bayesian inference is intractable for
DGPs, motivating the use of various approximations. We show that the
application of simplifying mean-field assumptions across the hierarchy leads to
the layers of a DGP collapsing to near-deterministic transformations. We argue
that such an inference scheme is suboptimal, not taking advantage of the
potential of the model to discover the compositional structure in the data. To
address this issue, we examine alternative variational inference schemes
allowing for dependencies across different layers and discuss their advantages
and limitations.Comment: 17 page
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