169 research outputs found
Iterative Amortized Inference
Inference models are a key component in scaling variational inference to deep
latent variable models, most notably as encoder networks in variational
auto-encoders (VAEs). By replacing conventional optimization-based inference
with a learned model, inference is amortized over data examples and therefore
more computationally efficient. However, standard inference models are
restricted to direct mappings from data to approximate posterior estimates. The
failure of these models to reach fully optimized approximate posterior
estimates results in an amortization gap. We aim toward closing this gap by
proposing iterative inference models, which learn to perform inference
optimization through repeatedly encoding gradients. Our approach generalizes
standard inference models in VAEs and provides insight into several empirical
findings, including top-down inference techniques. We demonstrate the inference
optimization capabilities of iterative inference models and show that they
outperform standard inference models on several benchmark data sets of images
and text.Comment: International Conference on Machine Learning (ICML) 201
Sparse estimation of large covariance matrices via a nested Lasso penalty
The paper proposes a new covariance estimator for large covariance matrices
when the variables have a natural ordering. Using the Cholesky decomposition of
the inverse, we impose a banded structure on the Cholesky factor, and select
the bandwidth adaptively for each row of the Cholesky factor, using a novel
penalty we call nested Lasso. This structure has more flexibility than regular
banding, but, unlike regular Lasso applied to the entries of the Cholesky
factor, results in a sparse estimator for the inverse of the covariance matrix.
An iterative algorithm for solving the optimization problem is developed. The
estimator is compared to a number of other covariance estimators and is shown
to do best, both in simulations and on a real data example. Simulations show
that the margin by which the estimator outperforms its competitors tends to
increase with dimension.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS139 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Model Selection for Signal Processing: a Minimum Error Approach and a General Performance Analysis
Estimation of the number of signals in the presence of noise is an important
problem in several areas of statistical signal processing. There are a number
of modern works on the design of an optimal solution to this problem in terms
of some criteria. Each criterion generates a model order selection (MOS)
algorithm. However, the minimum error probability criterion has not received
significant attention, although errors in the estimation of the number of
signals might directly affect the performance of the signal processing system
as a whole. In this paper, we propose a new approach to the design of MOS
algorithms partially based on the minimum error probability criterion. Also, we
pay a lot of attention to the performance and consistency analysis of the MOS
algorithms. In this study, an abridged error probability is used as a universal
performance measure of the MOS algorithms. We propose a theoretical framework
that makes it possible to obtain closed-form expressions for the abridged error
probabilities of a wide range of MOS algorithms. Moreover, a parametric
consistency analysis of the presented MOS algorithms is provided. Using the
obtained results, we provide a parametric optimization of the presented MOS
algorithms. Finally, we examinate a quasilikelihood (QL) approach to the design
and analysis of the MOS algorithms. The proposed theoretical framework is used
to obtain the abridged error probabilities as functions of the unknown signal
parameter. These functions, in turn, allow us to find the scope of the QL
approach.Comment: improved presentatio
Functional Regression
Functional data analysis (FDA) involves the analysis of data whose ideal
units of observation are functions defined on some continuous domain, and the
observed data consist of a sample of functions taken from some population,
sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the
development of this field, which has accelerated in the past 10 years to become
one of the fastest growing areas of statistics, fueled by the growing number of
applications yielding this type of data. One unique characteristic of FDA is
the need to combine information both across and within functions, which Ramsay
and Silverman called replication and regularization, respectively. This article
will focus on functional regression, the area of FDA that has received the most
attention in applications and methodological development. First will be an
introduction to basis functions, key building blocks for regularization in
functional regression methods, followed by an overview of functional regression
methods, split into three types: [1] functional predictor regression
(scalar-on-function), [2] functional response regression (function-on-scalar)
and [3] function-on-function regression. For each, the role of replication and
regularization will be discussed and the methodological development described
in a roughly chronological manner, at times deviating from the historical
timeline to group together similar methods. The primary focus is on modeling
and methodology, highlighting the modeling structures that have been developed
and the various regularization approaches employed. At the end is a brief
discussion describing potential areas of future development in this field
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of
predefined fiducial points. Despite recent success in this field,
higher-ordered structural modeling to capture implicit or explicit
relationships among anatomical landmarks has not been adequately exploited. In
this work, we present a new topology-adapting deep graph learning approach for
accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection.
The proposed method constructs graph signals leveraging both local image
features and global shape features. The adaptive graph topology naturally
explores and lands on task-specific structures which are learned end-to-end
with two Graph Convolutional Networks (GCNs). Extensive experiments are
conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as
well as three real-world X-ray medical datasets (Cephalometric (public), Hand
and Pelvis). Quantitative results comparing with the previous state-of-the-art
approaches across all studied datasets indicating the superior performance in
both robustness and accuracy. Qualitative visualizations of the learned graph
topologies demonstrate a physically plausible connectivity laying behind the
landmarks.Comment: Accepted to ECCV-20. Camera-ready with supplementary materia
Iterative Amortized Inference
Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs). By replacing conventional optimization-based inference with a learned model, inference is amortized over data examples and therefore more computationally efficient. However, standard inference models are restricted to direct mappings from data to approximate posterior estimates. The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap. We aim toward closing this gap by proposing iterative inference models, which learn to perform inference optimization through repeatedly encoding gradients. Our approach generalizes standard inference models in VAEs and provides insight into several empirical findings, including top-down inference techniques. We demonstrate the inference optimization capabilities of iterative inference models and show that they outperform standard inference models on several benchmark data sets of images and text
Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of
large-scale structured data, especially those related to complex domains such
as networks and graphs, are one of the key questions in modern machine
learning. Graph signal processing (GSP), a vibrant branch of signal processing
models and algorithms that aims at handling data supported on graphs, opens new
paths of research to address this challenge. In this article, we review a few
important contributions made by GSP concepts and tools, such as graph filters
and transforms, to the development of novel machine learning algorithms. In
particular, our discussion focuses on the following three aspects: exploiting
data structure and relational priors, improving data and computational
efficiency, and enhancing model interpretability. Furthermore, we provide new
perspectives on future development of GSP techniques that may serve as a bridge
between applied mathematics and signal processing on one side, and machine
learning and network science on the other. Cross-fertilization across these
different disciplines may help unlock the numerous challenges of complex data
analysis in the modern age
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