92,734 research outputs found
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in
machine-learning research. The key challenges are learning representations and
formulating inference methods that take into account the complementarity and
redundancy of various information sources. In this paper we formulate a
variational autoencoder based multi-source learning framework in which each
encoder is conditioned on a different information source. This allows us to
relate the sources via the shared latent variables by computing divergence
measures between individual source's posterior approximations. We explore a
variety of options to learn these encoders and to integrate the beliefs they
compute into a consistent posterior approximation. We visualise learned beliefs
on a toy dataset and evaluate our methods for learning shared representations
and structured output prediction, showing trade-offs of learning separate
encoders for each information source. Furthermore, we demonstrate how conflict
detection and redundancy can increase robustness of inference in a multi-source
setting.Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence
(AAAI) 201
XML document design via GN-DTD
Designing a well-structured XML document is important for the sake of readability and maintainability. More importantly, this will avoid data redundancies and update anomalies when maintaining a large quantity of XML based documents. In this paper, we propose a method to improve XML structural design by adopting graphical notations for Document Type Definitions (GN-DTD), which is used to describe the structure of an XML document at the schema level. Multiples levels of normal forms for GN-DTD are proposed on the basis of conceptual model approaches and theories of normalization. The normalization rules are applied to transform a poorly designed XML document into a well-designed based on normalized GN-DTD, which is illustrated through examples
Nonlinear approximation with nonstationary Gabor frames
We consider sparseness properties of adaptive time-frequency representations
obtained using nonstationary Gabor frames (NSGFs). NSGFs generalize classical
Gabor frames by allowing for adaptivity in either time or frequency. It is
known that the concept of painless nonorthogonal expansions generalizes to the
nonstationary case, providing perfect reconstruction and an FFT based
implementation for compactly supported window functions sampled at a certain
density. It is also known that for some signal classes, NSGFs with flexible
time resolution tend to provide sparser expansions than can be obtained with
classical Gabor frames. In this article we show, for the continuous case, that
sparseness of a nonstationary Gabor expansion is equivalent to smoothness in an
associated decomposition space. In this way we characterize signals with sparse
expansions relative to NSGFs with flexible time resolution. Based on this
characterization we prove an upper bound on the approximation error occurring
when thresholding the coefficients of the corresponding frame expansions. We
complement the theoretical results with numerical experiments, estimating the
rate of approximation obtained from thresholding the coefficients of both
stationary and nonstationary Gabor expansions.Comment: 19 pages, 2 figure
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
The goal of supervised feature selection is to find a subset of input
features that are responsible for predicting output values. The least absolute
shrinkage and selection operator (Lasso) allows computationally efficient
feature selection based on linear dependency between input features and output
values. In this paper, we consider a feature-wise kernelized Lasso for
capturing non-linear input-output dependency. We first show that, with
particular choices of kernel functions, non-redundant features with strong
statistical dependence on output values can be found in terms of kernel-based
independence measures. We then show that the globally optimal solution can be
efficiently computed; this makes the approach scalable to high-dimensional
problems. The effectiveness of the proposed method is demonstrated through
feature selection experiments with thousands of features.Comment: 18 page
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