37,564 research outputs found
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Probabilistic graphical models are a central tool in AI; however, they are
generally not as expressive as deep neural models, and inference is notoriously
hard and slow. In contrast, deep probabilistic models such as sum-product
networks (SPNs) capture joint distributions in a tractable fashion, but still
lack the expressive power of intractable models based on deep neural networks.
Therefore, we introduce conditional SPNs (CSPNs), conditional density
estimators for multivariate and potentially hybrid domains which allow
harnessing the expressive power of neural networks while still maintaining
tractability guarantees. One way to implement CSPNs is to use an existing SPN
structure and condition its parameters on the input, e.g., via a deep neural
network. This approach, however, might misrepresent the conditional
independence structure present in data. Consequently, we also develop a
structure-learning approach that derives both the structure and parameters of
CSPNs from data. Our experimental evidence demonstrates that CSPNs are
competitive with other probabilistic models and yield superior performance on
multilabel image classification compared to mean field and mixture density
networks. Furthermore, they can successfully be employed as building blocks for
structured probabilistic models, such as autoregressive image models.Comment: 13 pages, 6 figure
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership
A method for implicit variable selection in mixture-of-experts frameworks is proposed.
We introduce a prior structure where information is taken from a set of independent
covariates. Robust class membership predictors are identified using a normal gamma
prior. The resulting model setup is used in a finite mixture of Bernoulli distributions
to find homogenous clusters of women in Mozambique based on their information
sources on HIV. Fully Bayesian inference is carried out via the implementation of a
Gibbs sampler
Futures Studies in the Interactive Society
This book consists of papers which were prepared within the framework of the research project (No. T 048539) entitled Futures Studies in the Interactive Society (project leader: Éva Hideg) and funded by the Hungarian Scientific Research Fund (OTKA) between 2005 and 2009. Some discuss the theoretical and methodological questions of futures studies and foresight; others present new approaches to or
procedures of certain questions which are very important and topical from the perspective of forecast and foresight practice. Each study was conducted in pursuit of improvement in futures fields
Prognosis of the state of health of a person under spaceflight conditions
Methods of predicting the state of health and human efficiency during space flight are discussed. Diversity of reactions to the same conditions, development of extrapolation methods of prediction, and isolation of informative physiological indexes are among the factors considered
Composite load spectra for select space propulsion structural components
The objective of this program is to develop generic load models with multiple levels of progressive sophistication to simulate the composite (combined) load spectra that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades, and liquid oxygen posts and system ducting. The first approach will consist of using state of the art probabilistic methods to describe the individual loading conditions and combinations of these loading conditions to synthesize the composite load spectra simulation. The second approach will consist of developing coupled models for composite load spectra simulation which combine the deterministic models for composite load dynamic, acoustic, high pressure, and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients will then be determined using advanced probabilistic simulation methods with and without strategically selected experimental data
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