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IBM Deep Learning Service
[article]
2017
arXiv
pre-print
Preserved Fulltext
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision. At the same time, the "as-a-Service"-based business model on the cloud is fundamentally transforming the information technology industry. These two trends: deep learning, and "as-a-service" are colliding to give rise to a new business model for cognitive application delivery: deep learning as a service
arXiv:1709.05871v1
fatcat:wgifwcuqjfghxj7ounvapkh3du
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... in the cloud. In this paper, we will discuss the details of the software architecture behind IBM's deep learning as a service (DLaaS). DLaaS provides developers the flexibility to use popular deep learning libraries such as Caffe, Torch and TensorFlow, in the cloud in a scalable and resilient manner with minimal effort. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes. A resource provisioning layer enables flexible job management on heterogeneous resources, such as graphics processing units (GPUs) and central processing units (CPUs), in an infrastructure as a service (IaaS) cloud.
Exploratory Study of Scientific Visualization Techniques for Program Visualization
[chapter]
2001
Lecture Notes in Computer Science
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This paper presents a unique point-of-view for program visualization, namely, the use of scientific visualization techniques for program visualization. This paper is exploratory in nature. Its primary contribution is to re-examine program visualization from a scientific visualization point-of-view. This paper reveals that specific visualization techniques such as animation, isolines, program slicing, dimensional reduction, glyphs and color maps may be considered for program visualization. In
doi:10.1007/3-540-45718-6_75
fatcat:ke5ycykhw5eixkqctha6gjyyli
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... ition, some features of AVS/Express that may be used for program visualization are discussed. Lastly, comments regarding emotional color spaces are made.
Statistical and Dempster-Shafer techniques in testing structural integrity of aerospace structures
2003
Smart Nondestructive Evaluation and Health Monitoring of Structural and Biological Systems II
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a c b e d f h g p i q b 4 r P s u t H v q f x w y f x ¤ f x f h i q g q 9 a s 1 f x f x ) g q i ¥ y d y e y g p y 1 f h g H d i j R g q f h f b l k m S n r R o q p ) f x f r i s f h g x u t ¥ f u k e f ...
) ¬ ¶ ¸ h µ § ¥ l q ¥ % § § ¶ ¾ p ¥ r ³ y ¢ © H 8 ¥ l q D § R © r p F ± ) 6 l ¥ 6 p ¿ u 6 ¸ h ¼ s h © ª D h l © ¥ H Ð e ³ Þ ß R © r 6 p AE h © q l ¡ ¤ F ª D G · © G ¢ u D 6 ¥ l p ¬ ¨ D ¼ r · l © U © y ...
doi:10.1117/12.483959
fatcat:3ckbg2ckl5gpjdmh42dht3zpri
STATISTICAL AND DEMPSTER-SHAFER TECHNIQUES IN TESTING STRUCTURAL INTEGRITY OF AEROSPACE STRUCTURES
2001
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems
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Osegueda, Seetharami R. Seelam, Ana C. Holguin, Vladik Kreinovich, Chin-Wang Tao, and Hung T. Nguyen This article is available at DigitalCommons@UTEP: https://digitalcommons.utep.edu/cs_techrep/496 ...
i R u D R u d A e g f t D R d h j i D k l U A h m R n k u o q p q n ) r t D R ) t s u i t h v p R p q h w ¤ R s q h d ) n x y j l z A h j y D f { j | i t h v p R p q h n } ĩ 8 H } ' t { X 3 Q u 8 % ' i ...
doi:10.1142/s0218488501001204
fatcat:d3kyb3sawvettnltyw2zkl2gdq
Tools for scalable performance analysis on Petascale systems
2009
2009 IEEE International Symposium on Parallel & Distributed Processing
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Seetharami R. Seelam, Ph.D., is a post-doctoral research staff member at the ffiM T. J. Watson Research Center. ...
doi:10.1109/ipdps.2009.5160865
dblp:conf/ipps/ChungSML09
fatcat:2y426rdprbb4pa2u4m5g4oahza
Author index
2006
2006 IEEE International Conference on Cluster Computing
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, Seetharami R. ...
R. ...
doi:10.1109/clustr.2006.311921
fatcat:vmbbimypuze7ncjqfonu4po5l4
Topology-aware GPU scheduling for learning workloads in cloud environments
2017
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '17
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Job's slowdown relative to the best performing con guration.SC17, November 12-17, 2017, Denver, CO, USA Marcelo Amaral, Jordà Polo, David Carrera, Seetharami Seelam, and Malgorzata Steinder 5.5.2 Scenario ...
0 , P 1 , C) 5: (P 0 .I b , P 1 .I b ) ← getInter(t ask, P 0 , P 1 , A.pr of il e) 6: (P 0 .ω d , P 1 .ω d ) ← getFragmentation(P 0 , P 1 , A) 7: if (U(t ask , P 0 ) ≥ U(t ask , P 1 )) and (const r ...
doi:10.1145/3126908.3126933
dblp:conf/sc/AmaralPCSS17
fatcat:vu4i6hn7jbbtjou3f64bkbg4xa
Composing Model-Based Analysis Tools (Dagstuhl Seminar 19481)
2020
Dagstuhl Reports
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In Seetharami Seelam, Petr Tuma, Giuliano Casale, Tony Field, and José Nelson Amaral, editors, ACM/SPEC International Conference on Performance Engineering, ICPE, pages 311-314. ...
In Michael Kohlhase, Moa Johansson, Bruce R. Miller, Leonardo de Moura, and Frank Wm. ...
doi:10.4230/dagrep.9.11.97
dblp:journals/dagstuhl-reports/DuranHPTZ19
fatcat:f3noqtkj3bg6zdy5fym5yrzuda
Throttling I/O Streams to Accelerate File-IO Performance
[chapter]
2007
Lecture Notes in Computer Science
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Araunagiri, R. Portillo, and M. Ruiz, for their valuable feedback. ...
doi:10.1007/978-3-540-75444-2_67
fatcat:ivn3fummmveb3oy5j2p6tuyoue
Statistical and Dempster-Shafer Techniques in Testing Structural Integrity of Aerospace Structures
2001
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems
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Osegueda, Seetharami R. Seelam, Ana C. Holguin, Vladik Kreinovich, Chin-Wang Tao, and Hung T. Nguyen This article is available at DigitalCommons@UTEP: https://digitalcommons.utep.edu/cs_techrep/496 ...
i R u D R u d A e g f t D R d h j i D k l U A h m R n k u o q p q n ) r t D R ) t s u i t h v p R p q h w ¤ R s q h d ) n x y j l z A h j y D f { j | i t h v p R p q h n } ĩ 8 H } ' t { X 3 Q u 8 % ' i ...
doi:10.1016/s0218-4885(01)00120-4
fatcat:kekonfncxjhtvjefn3wypsmngm
Railgun: managing large streaming windows under MAD requirements
[article]
2021
arXiv
pre-print
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Ter- public - April 21 - 24, 2013, Seetharami Seelam, Petr Tuma, Giuliano Casale, Tony
williger. 2015. Trill: Engineering a Library for Diverse Analytics. ...
Spark Closer to Bare Metal. https://databricks.com/blog/2015/04/28/project-
[46] Georgios Theodorakis, Alexandros Koliousis, Peter R. Pietzuch, and Holger Pirk. ...
arXiv:2106.12626v1
fatcat:otrohxszszay7dwp4cdmcrfqke