97,268 research outputs found
Learning Generative Models with Visual Attention
Attention has long been proposed by psychologists as important for
effectively dealing with the enormous sensory stimulus available in the
neocortex. Inspired by the visual attention models in computational
neuroscience and the need of object-centric data for generative models, we
describe for generative learning framework using attentional mechanisms.
Attentional mechanisms can propagate signals from region of interest in a scene
to an aligned canonical representation, where generative modeling takes place.
By ignoring background clutter, generative models can concentrate their
resources on the object of interest. Our model is a proper graphical model
where the 2D Similarity transformation is a part of the top-down process. A
ConvNet is employed to provide good initializations during posterior inference
which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our
model can robustly attend to face regions of novel test subjects. More
importantly, our model can learn generative models of new faces from a novel
dataset of large images where the face locations are not known.Comment: In the proceedings of Neural Information Processing Systems, 201
Bayesian analysis of ranking data with the constrained Extended Plackett-Luce model
Multistage ranking models, including the popular Plackett-Luce distribution
(PL), rely on the assumption that the ranking process is performed
sequentially, by assigning the positions from the top to the bottom one
(forward order). A recent contribution to the ranking literature relaxed this
assumption with the addition of the discrete-valued reference order parameter,
yielding the novel Extended Plackett-Luce model (EPL). Inference on the EPL and
its generalization into a finite mixture framework was originally addressed
from the frequentist perspective. In this work, we propose the Bayesian
estimation of the EPL with order constraints on the reference order parameter.
The proposed restrictions reflect a meaningful rank assignment process. By
combining the restrictions with the data augmentation strategy and the
conjugacy of the Gamma prior distribution with the EPL, we facilitate the
construction of a tuned joint Metropolis-Hastings algorithm within Gibbs
sampling to simulate from the posterior distribution. The Bayesian approach
allows to address more efficiently the inference on the additional
discrete-valued parameter and the assessment of its estimation uncertainty. The
usefulness of the proposal is illustrated with applications to simulated and
real datasets.Comment: 20 pages, 4 figures, 4 tables. arXiv admin note: substantial text
overlap with arXiv:1803.0288
An overview of the VRS virtual platform
This paper provides an overview of the development of the virtual platform within the European Commission funded VRShips-ROPAX (VRS) project. This project is a major collaboration of approximately 40 industrial, regulatory, consultancy and academic partners with the objective of producing two novel platforms. A physical platform will be designed and produced representing a scale model of a novel ROPAX vessel with the following criteria: 2000 passengers; 400 cabins; 2000 nautical mile range, and a service speed of 38 knots. The aim of the virtual platform is to demonstrate that vessels may be designed to meet these criteria, which was not previously possible using individual tools and conventional design approaches. To achieve this objective requires the integration of design and simulation tools representing concept, embodiment, detail, production, and operation life-phases into the virtual platform, to enable distributed design activity to be undertaken. The main objectives for the development of the virtual platform are described, followed by the discussion of the techniques chosen to address the objectives, and finally a description of a use-case for the platform. Whilst the focus of the VRS virtual platform was to facilitate the design of ROPAX vessels, the components within the platform are entirely generic and may be applied to the distributed design of any type of vessel, or other complex made-to-order products
String and Membrane Gaussian Processes
In this paper we introduce a novel framework for making exact nonparametric
Bayesian inference on latent functions, that is particularly suitable for Big
Data tasks. Firstly, we introduce a class of stochastic processes we refer to
as string Gaussian processes (string GPs), which are not to be mistaken for
Gaussian processes operating on text. We construct string GPs so that their
finite-dimensional marginals exhibit suitable local conditional independence
structures, which allow for scalable, distributed, and flexible nonparametric
Bayesian inference, without resorting to approximations, and while ensuring
some mild global regularity constraints. Furthermore, string GP priors
naturally cope with heterogeneous input data, and the gradient of the learned
latent function is readily available for explanatory analysis. Secondly, we
provide some theoretical results relating our approach to the standard GP
paradigm. In particular, we prove that some string GPs are Gaussian processes,
which provides a complementary global perspective on our framework. Finally, we
derive a scalable and distributed MCMC scheme for supervised learning tasks
under string GP priors. The proposed MCMC scheme has computational time
complexity and memory requirement , where
is the data size and the dimension of the input space. We illustrate the
efficacy of the proposed approach on several synthetic and real-world datasets,
including a dataset with millions input points and attributes.Comment: To appear in the Journal of Machine Learning Research (JMLR), Volume
1
Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data
We propose an estimation approach to analyse correlated functional data which
are observed on unequal grids or even sparsely. The model we use is a
functional linear mixed model, a functional analogue of the linear mixed model.
Estimation is based on dimension reduction via functional principal component
analysis and on mixed model methodology. Our procedure allows the decomposition
of the variability in the data as well as the estimation of mean effects of
interest and borrows strength across curves. Confidence bands for mean effects
can be constructed conditional on estimated principal components. We provide
R-code implementing our approach. The method is motivated by and applied to
data from speech production research
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
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