4,781 research outputs found
Magnification Control in Self-Organizing Maps and Neural Gas
We consider different ways to control the magnification in self-organizing
maps (SOM) and neural gas (NG). Starting from early approaches of magnification
control in vector quantization, we then concentrate on different approaches for
SOM and NG. We show that three structurally similar approaches can be applied
to both algorithms: localized learning, concave-convex learning, and winner
relaxing learning. Thereby, the approach of concave-convex learning in SOM is
extended to a more general description, whereas the concave-convex learning for
NG is new. In general, the control mechanisms generate only slightly different
behavior comparing both neural algorithms. However, we emphasize that the NG
results are valid for any data dimension, whereas in the SOM case the results
hold only for the one-dimensional case.Comment: 24 pages, 4 figure
Recommending Learning Algorithms and Their Associated Hyperparameters
The success of machine learning on a given task dependson, among other
things, which learning algorithm is selected and its associated
hyperparameters. Selecting an appropriate learning algorithm and setting its
hyperparameters for a given data set can be a challenging task, especially for
users who are not experts in machine learning. Previous work has examined using
meta-features to predict which learning algorithm and hyperparameters should be
used. However, choosing a set of meta-features that are predictive of algorithm
performance is difficult. Here, we propose to apply collaborative filtering
techniques to learning algorithm and hyperparameter selection, and find that
doing so avoids determining which meta-features to use and outperforms
traditional meta-learning approaches in many cases.Comment: Short paper--2 pages, 2 table
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Anomaly Detection in Cloud Components
Cloud platforms, under the hood, consist of a complex inter-connected stack
of hardware and software components. Each of these components can fail which
may lead to an outage. Our goal is to improve the quality of Cloud services
through early detection of such failures by analyzing resource utilization
metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood
function to detect anomalies in various multi-dimensional time series and
achieved high performance.Comment: Accepted for publication in Proceedings of the IEEE International
Conference on Cloud Computing (CLOUD 2020). Fix dataset descriptio
Blind source separation of multichannel neuromagnetic responses
Magnetoencephalography (MEG) is a functional brain imaging technique with millisecond
temporal resolution and millimeter spatial sensitivity. The high temporal resolution of MEG
compared to fMRI and PET (milliseconds vs. seconds and tens of seconds) makes it ideal for
measuring the precise time of neuronal responses, thereby o!ering a powerful tool for studying
temporal dynamics. We applied blind-source separation (BSS) to continuous 122-channel
human magnetoencephalographic data from two subjects and "ve tasks. We demonstrate that
without using any domain-speci"c knowledge and without making the common assumption of
single- or multiple-current dipole sources, BSS is capable of separating non-neuronal noise
sources from neuronal responses and also of separating neuronal responses from di!erent
sensory modalities, and from di!erent processing stages within a given modalit
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