1,480 research outputs found
PixelGAN Autoencoders
In this paper, we describe the "PixelGAN autoencoder", a generative
autoencoder in which the generative path is a convolutional autoregressive
neural network on pixels (PixelCNN) that is conditioned on a latent code, and
the recognition path uses a generative adversarial network (GAN) to impose a
prior distribution on the latent code. We show that different priors result in
different decompositions of information between the latent code and the
autoregressive decoder. For example, by imposing a Gaussian distribution as the
prior, we can achieve a global vs. local decomposition, or by imposing a
categorical distribution as the prior, we can disentangle the style and content
information of images in an unsupervised fashion. We further show how the
PixelGAN autoencoder with a categorical prior can be directly used in
semi-supervised settings and achieve competitive semi-supervised classification
results on the MNIST, SVHN and NORB datasets
A Selective Overview of Deep Learning
Deep learning has arguably achieved tremendous success in recent years. In
simple words, deep learning uses the composition of many nonlinear functions to
model the complex dependency between input features and labels. While neural
networks have a long history, recent advances have greatly improved their
performance in computer vision, natural language processing, etc. From the
statistical and scientific perspective, it is natural to ask: What is deep
learning? What are the new characteristics of deep learning, compared with
classical methods? What are the theoretical foundations of deep learning? To
answer these questions, we introduce common neural network models (e.g.,
convolutional neural nets, recurrent neural nets, generative adversarial nets)
and training techniques (e.g., stochastic gradient descent, dropout, batch
normalization) from a statistical point of view. Along the way, we highlight
new characteristics of deep learning (including depth and over-parametrization)
and explain their practical and theoretical benefits. We also sample recent
results on theories of deep learning, many of which are only suggestive. While
a complete understanding of deep learning remains elusive, we hope that our
perspectives and discussions serve as a stimulus for new statistical research
Degrees of Freedom in Deep Neural Networks
In this paper, we explore degrees of freedom in deep sigmoidal neural
networks. We show that the degrees of freedom in these models is related to the
expected optimism, which is the expected difference between test error and
training error. We provide an efficient Monte-Carlo method to estimate the
degrees of freedom for multi-class classification methods. We show degrees of
freedom are lower than the parameter count in a simple XOR network. We extend
these results to neural nets trained on synthetic and real data, and
investigate impact of network's architecture and different regularization
choices. The degrees of freedom in deep networks are dramatically smaller than
the number of parameters, in some real datasets several orders of magnitude.
Further, we observe that for fixed number of parameters, deeper networks have
less degrees of freedom exhibiting a regularization-by-depth
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
Recent work in graph neural networks (GNNs) has led to improvements in
molecular activity and property prediction tasks. Unfortunately, GNNs often
fail to capture the relative importance of interactions between molecular
substructures, in part due to the absence of efficient intermediate pooling
steps. To address these issues, we propose LaPool (Laplacian Pooling), a novel,
data-driven, and interpretable hierarchical graph pooling method that takes
into account both node features and graph structure to improve molecular
representation. We benchmark LaPool on molecular graph prediction and
understanding tasks and show that it outperforms recent GNNs. Interestingly,
LaPool also remains competitive on non-molecular tasks. Both quantitative and
qualitative assessments are done to demonstrate LaPool's improved
interpretability and highlight its potential benefits in drug design. Finally,
we demonstrate LaPool's utility for the generation of valid and novel molecules
by incorporating it into an adversarial autoencoder.Comment: 11 pages, with Appendice
Gated networks: an inventory
Gated networks are networks that contain gating connections, in which the
outputs of at least two neurons are multiplied. Initially, gated networks were
used to learn relationships between two input sources, such as pixels from two
images. More recently, they have been applied to learning activity recognition
or multi-modal representations. The aims of this paper are threefold: 1) to
explain the basic computations in gated networks to the non-expert, while
adopting a standpoint that insists on their symmetric nature. 2) to serve as a
quick reference guide to the recent literature, by providing an inventory of
applications of these networks, as well as recent extensions to the basic
architecture. 3) to suggest future research directions and applications.Comment: Unpublished manuscript, 17 page
Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders
Autoencoders are widely used for unsupervised learning and as a
regularization scheme in semi-supervised learning. However, theoretical
understanding of their generalization properties and of the manner in which
they can assist supervised learning has been lacking. We utilize recent
advances in the theory of deep learning generalization, together with a novel
reconstruction loss, to provide generalization bounds for autoencoders. To the
best of our knowledge, this is the first such bound. We further show that,
under appropriate assumptions, an autoencoder with good generalization
properties can improve any semi-supervised learning scheme. We support our
theoretical results with empirical demonstrations.Comment: Submitted to COLT 201
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover latent thematic structure in discrete data.
Despite the success of these models on textual data, they have not generalized
as well to image data, in part because of the spatial and temporal structure
that may exist in an image stream.
We introduce a novel unsupervised machine learning framework that
incorporates the ability of convolutional autoencoders to discover features
from images that directly encode spatial information, within a Bayesian
nonparametric topic model that discovers meaningful latent patterns within
discrete data. By using this hybrid framework, we overcome the fundamental
dependency of traditional topic models on rigidly hand-coded data
representations, while simultaneously encoding spatial dependency in our topics
without adding model complexity. We apply this model to the motivating
application of high-level scene understanding and mission summarization for
exploratory marine robots. Our experiments on a seafloor dataset collected by a
marine robot show that the proposed hybrid framework outperforms current
state-of-the-art approaches on the task of unsupervised seafloor terrain
characterization.Comment: 8 page
Learning Ordered Representations with Nested Dropout
In this paper, we study ordered representations of data in which different
dimensions have different degrees of importance. To learn these representations
we introduce nested dropout, a procedure for stochastically removing coherent
nested sets of hidden units in a neural network. We first present a sequence of
theoretical results in the simple case of a semi-linear autoencoder. We
rigorously show that the application of nested dropout enforces identifiability
of the units, which leads to an exact equivalence with PCA. We then extend the
algorithm to deep models and demonstrate the relevance of ordered
representations to a number of applications. Specifically, we use the ordered
property of the learned codes to construct hash-based data structures that
permit very fast retrieval, achieving retrieval in time logarithmic in the
database size and independent of the dimensionality of the representation. This
allows codes that are hundreds of times longer than currently feasible for
retrieval. We therefore avoid the diminished quality associated with short
codes, while still performing retrieval that is competitive in speed with
existing methods. We also show that ordered representations are a promising way
to learn adaptive compression for efficient online data reconstruction.Comment: 11 pages, 5 figures. Submitted for publicatio
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
This paper presents a new supervised classification algorithm for remotely
sensed hyperspectral image (HSI) which integrates spectral and spatial
information in a unified Bayesian framework. First, we formulate the HSI
classification problem from a Bayesian perspective. Then, we adopt a
convolutional neural network (CNN) to learn the posterior class distributions
using a patch-wise training strategy to better use the spatial information.
Next, spatial information is further considered by placing a spatial smoothness
prior on the labels. Finally, we iteratively update the CNN parameters using
stochastic gradient decent (SGD) and update the class labels of all pixel
vectors using an alpha-expansion min-cut-based algorithm. Compared with other
state-of-the-art methods, the proposed classification method achieves better
performance on one synthetic dataset and two benchmark HSI datasets in a number
of experimental settings
Deep Learning on Graphs: A Survey
Deep learning has been shown to be successful in a number of domains, ranging
from acoustics, images, to natural language processing. However, applying deep
learning to the ubiquitous graph data is non-trivial because of the unique
characteristics of graphs. Recently, substantial research efforts have been
devoted to applying deep learning methods to graphs, resulting in beneficial
advances in graph analysis techniques. In this survey, we comprehensively
review the different types of deep learning methods on graphs. We divide the
existing methods into five categories based on their model architectures and
training strategies: graph recurrent neural networks, graph convolutional
networks, graph autoencoders, graph reinforcement learning, and graph
adversarial methods. We then provide a comprehensive overview of these methods
in a systematic manner mainly by following their development history. We also
analyze the differences and compositions of different methods. Finally, we
briefly outline the applications in which they have been used and discuss
potential future research directions.Comment: Accepted by Transactions on Knowledge and Data Engineering. 24 pages,
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