359,561 research outputs found
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
Deep Tree Transductions - A Short Survey
The paper surveys recent extensions of the Long-Short Term Memory networks to
handle tree structures from the perspective of learning non-trivial forms of
isomorph structured transductions. It provides a discussion of modern TreeLSTM
models, showing the effect of the bias induced by the direction of tree
processing. An empirical analysis is performed on real-world benchmarks,
highlighting how there is no single model adequate to effectively approach all
transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep
Learning (INNSBDDL 2019). arXiv admin note: text overlap with
arXiv:1809.0909
Network Representation Learning: From Traditional Feature Learning to Deep Learning
Network representation learning (NRL) is an effective graph analytics
technique and promotes users to deeply understand the hidden characteristics of
graph data. It has been successfully applied in many real-world tasks related
to network science, such as social network data processing, biological
information processing, and recommender systems. Deep Learning is a powerful
tool to learn data features. However, it is non-trivial to generalize deep
learning to graph-structured data since it is different from the regular data
such as pictures having spatial information and sounds having temporal
information. Recently, researchers proposed many deep learning-based methods in
the area of NRL. In this survey, we investigate classical NRL from traditional
feature learning method to the deep learning-based model, analyze relationships
between them, and summarize the latest progress. Finally, we discuss open
issues considering NRL and point out the future directions in this field
A Review of Graph Neural Networks and Their Applications in Power Systems
Deep neural networks have revolutionized many machine learning tasks in power
systems, ranging from pattern recognition to signal processing. The data in
these tasks is typically represented in Euclidean domains. Nevertheless, there
is an increasing number of applications in power systems, where data are
collected from non-Euclidean domains and represented as graph-structured data
with high dimensional features and interdependency among nodes. The complexity
of graph-structured data has brought significant challenges to the existing
deep neural networks defined in Euclidean domains. Recently, many publications
generalizing deep neural networks for graph-structured data in power systems
have emerged. In this paper, a comprehensive overview of graph neural networks
(GNNs) in power systems is proposed. Specifically, several classical paradigms
of GNNs structures (e.g., graph convolutional networks) are summarized, and key
applications in power systems, such as fault scenario application, time series
prediction, power flow calculation, and data generation are reviewed in detail.
Furthermore, main issues and some research trends about the applications of
GNNs in power systems are discussed
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