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Robust Large-Scale Machine Learning in the Cloud
2016
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16
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The convergence behavior of many distributed machine learning (ML) algorithms can be sensitive to the number of machines being used or to changes in the computing environment. ...
As a result, scaling to a large number of machines can be challenging. ...
discussions about the SCD algorithm and system design. ...
doi:10.1145/2939672.2939790
dblp:conf/kdd/RendleFSS16
fatcat:bkzqcufugvaspkn2x7bsd5zoiu
Compiling machine learning algorithms with SystemML
2013
Proceedings of the 4th annual Symposium on Cloud Computing - SOCC '13
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We chose algorithms that are robust and scale to large, and potentially sparse data with large numbers of features. ...
The flexibility provided by the high-level language leads to significant productivity gains for developers of machine learning algorithms in terms of lines of code and development time compared to Java ...
We chose algorithms that are robust and scale to large, and potentially sparse data with large numbers of features. ...
doi:10.1145/2523616.2525965
dblp:conf/cloud/BoehmBERSTT13
fatcat:7uokmzc54rdc7iexuda2nkrmfm
CANFAR+Skytree: A Cloud Computing and Data Mining System for Astronomy
[article]
2013
arXiv
pre-print
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Because Skytree scales to large data in linear runtime, this allows the full sophistication of the huge fields of data mining and machine learning to be applied to the hundreds of millions of objects that ...
At the Canadian Astronomy Data Centre, we have combined our cloud computing system, CANFAR, with the world's most advanced machine learning software, Skytree, to create the world's first cloud computing ...
This research used the facilities of the Canadian Astronomy Data Centre, operated by the National Research Council of Canada with the support of the Canadian Space Agency. ...
arXiv:1312.3996v1
fatcat:mhm6ffqi4vhkhpekhz5ujdcddu
Machine Learning Climate Model Dynamics: Offline versus Online Performance
[article]
2020
arXiv
pre-print
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Machine learning could improve upon the accuracy of some traditional physical parametrizations by learning from so-called global cloud-resolving models. ...
We compare the performance of two machine learning models, random forests (RF) and neural networks (NNs), at parametrizing the aggregate effect of moist physics in a 3 km resolution global simulation with ...
Acknowledgments and Disclosure of Funding The authors acknowledge the support of Vulcan, Inc. for funding this project. ...
arXiv:2011.03081v1
fatcat:6z7suykju5bxda7krjecrhruiu
Toward Antifragile Cloud Computing Infrastructures
2014
Procedia Computer Science
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Cloud computing systems are rapidly growing in scale and complexity. ...
Such large-scale, complex and dynamic cloud environments are prone to failures and performance anomalies. ...
In the context of cloud, the inherent complexity and large-scale nature of cloud infrastructures increase the likelihood of failure occurrence. ...
doi:10.1016/j.procs.2014.05.501
fatcat:wjpq6xnokfayzbgpq3j4os6oge
Distributed boosting for cloud detection
2016
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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In order to significantly improve the SPOT cloud detection and get rid of frequent manual re-labelings, we study a new automatic cloud detection technique that is adapted to large datasets. ...
in order to feed a catalogue with a binary cloud mask and an appropriate confidence measure. ...
The problem is therefore to learn a robust classifier from a large database containing ground truth masks. ...
doi:10.1109/igarss.2016.7729678
dblp:conf/igarss/GoffTWOS16
fatcat:po7634st6be47mnjcongsvmyie
Exterminating Computational Limits of Machine Learning with Merits of Serverless
2018
International Journal of Engineering Research and
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The trend of determining patterns using the various machine-learning, data mining, deep-learning or neural networks are limited due to the computational power of machines. ...
A serverless compute may give an optimal solution to the computational resources provided by the cloud. ...
technique
for
advanced
migration
Decrease
in
migration
time about 9.5%
Improve the
efficiency in
the large scale
service. ...
doi:10.17577/ijertv7is010147
fatcat:xsbi5flvkjgtjnxctkspzggxhu
OBJECT CLASSIFICATION VIA PLANAR ABSTRACTION
2016
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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We present a supervised machine learning approach for classification of objects from sampled point data. ...
The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar ...
We contribute a novel supervised machine learning method for the classification of objects acquired in indoor scenes. ...
doi:10.5194/isprsannals-iii-3-225-2016
fatcat:56otbm3kjrhmph4dms2fix5ueq
OBJECT CLASSIFICATION VIA PLANAR ABSTRACTION
2016
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The file type is application/pdf.
We present a supervised machine learning approach for classification of objects from sampled point data. ...
The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar ...
We contribute a novel supervised machine learning method for the classification of objects acquired in indoor scenes. ...
doi:10.5194/isprs-annals-iii-3-225-2016
fatcat:74xlipircfhuhoesxsrfxrurz4
Topological data analysis and machine learning
[article]
2022
arXiv
pre-print
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We present a concise yet (we hope) comprehensive review of applications of topological data analysis to physics and machine learning problems in physics including the detection of phase transitions. ...
There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. ...
FUNDING This research was supported by the National Research Foundation, Prime Minister's Office, Singapore and the Ministry of Education, Singapore under the Research Centres of Excellence Programme. ...
arXiv:2206.15075v1
fatcat:mx2jmrwiafhejm5a3mzgecia74
QoS-oriented Service Management in clouds for large scale industrial activity recognition
2011
2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR)
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Deploying the Activity Recognition Framework in a cloud infrastructure can therefore enable it for large scale industrial environments. ...
learning tools, attaining good recognition rates. ...
on an industrial large scale. ...
doi:10.1109/socpar.2011.6089156
dblp:conf/socpar/VoulodimosKGDKV11
fatcat:6xc7s65xz5cstftdoedkvaddka
An Incorporation of Artificial Intelligence Capabilities in Cloud Computing
2016
International Journal Of Engineering And Computer Science
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In this paper, discuss about artificial intelligence capabilities in cloud computing, in the form of cloud machine learning platforms and artificial intelligence cloud services. ...
They provides cloud machine learning platform and artificial intelligence cloud services like computer vision, powerful speech recognition, powerful text analysis, fast dynamic translation, smart search ...
Google Cloud Machine Learning Platform is a fast, large scale and easy to use Machine Learning Services. ...
doi:10.18535/ijecs/v5i11.63
fatcat:o5wbltjolfefpedjzcpgn62wzy
Guest Editors' Introduction to the Joint Special Section on Secure and Emerging Collaborative Computing and Intelligent Systems
2021
IEEE Transactions on Dependable and Secure Computing
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, machine learning robustness, authorizations, mobile computing, and deep learning for blockchain applications. ...
Ç T HE Internet coupled with recent advances in computing and information technologies, such as IoT, mobile edge/ cloud computing, cyber-physical-social systems, and artificial intelligence/machine learning ...
efficiently verifying large-scale virtual infrastructures in cloud and Network Functions Virtualization (NFV) against network isolation policies. ...
doi:10.1109/tdsc.2021.3076120
fatcat:jd4cuxgklbbfvebajnnnp4o35m
LIMPID: Large-Scale Image Processing Infrastructure
[article]
2020
Figshare
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The primary goal is to create a large scale distributed image processing infrastructure, the LIMPID, though a broad, interdisciplinary collaboration of researchers in databases, image analysis, and sciences ...
In addition, a core cloud-based platform will be created where custom image processing can be created, shared, modified, and executed on large-scale datasets and apply novel methods to minimize data movement ...
and Annotate in the
web-browser
• Large and multimodal
• Share
• Collaborate
• Analyze
D i f f i c u l t
A n a l y s i s
Condor Master (Collector)
Postgres 10.4, db = bisque
NFS /run/bisque ...
doi:10.6084/m9.figshare.11786931.v1
fatcat:5vsfd6bltzhrhdzeafnladjlre
CLASSIFICATION OF BIG POINT CLOUD DATA USING CLOUD COMPUTING
2015
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In machine learning, the problem is called classification. In addition, processing point data is becoming more and more challenging due to the growing data volume. ...
The popular cluster computing framework Apache Spark is used through the experiments and the promising results suggests a great potential of Apache Spark for large-scale point data processing. ...
ACKNOWLEDGEMENTS The authors would like to thank IGN to provide the mobile mapping data. This research work is supported by EU grant FP7-ICT-2011-318787 (IQmulus) and Amazon AWS grant. ...
doi:10.5194/isprsarchives-xl-3-w3-553-2015
fatcat:nwhqzpctvrf2vaoyp4jsmugvrm
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