On fast deep nets for AGI vision

Jurgen Schmidhuber; Dan Cireşan; Ueli Meier; Jonathan Masci; Alex Graves

doi:10.1007/978-3-642-22887-2_25

On fast deep nets for AGI vision

Jurgen Schmidhuber, Dan Cireşan, Ueli Meier, Jonathan Masci, Alex Graves

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

Artificial General Intelligence will not be general without computer vision. Biologically inspired adaptive vision models have started to outperform traditional pre-programmed methods: our fast deep / recurrent neural networks recently collected a string of 1st ranks in many important visual pattern recognition benchmarks: IJCNN traffic sign competition, NORB, CIFAR10, MNIST, three ICDAR handwriting competitions. We greatly profit from recent advances in computing hardware, complementing recent progress in the AGI theory of mathematically optimal universal problem solvers. © 2011 Springer-Verlag Berlin Heidelberg.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	243-246
Number of pages	4
DOIs	https://doi.org/10.1007/978-3-642-22887-2_25
State	Published - Aug 11 2011
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-22887-2_25

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title = "On fast deep nets for AGI vision",

abstract = "Artificial General Intelligence will not be general without computer vision. Biologically inspired adaptive vision models have started to outperform traditional pre-programmed methods: our fast deep / recurrent neural networks recently collected a string of 1st ranks in many important visual pattern recognition benchmarks: IJCNN traffic sign competition, NORB, CIFAR10, MNIST, three ICDAR handwriting competitions. We greatly profit from recent advances in computing hardware, complementing recent progress in the AGI theory of mathematically optimal universal problem solvers. {\textcopyright} 2011 Springer-Verlag Berlin Heidelberg.",

author = "Jurgen Schmidhuber and Dan Cire{\c s}an and Ueli Meier and Jonathan Masci and Alex Graves",

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year = "2011",

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AU - Schmidhuber, Jurgen

AU - Cireşan, Dan

AU - Meier, Ueli

AU - Masci, Jonathan

AU - Graves, Alex

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2011/8/11

Y1 - 2011/8/11

N2 - Artificial General Intelligence will not be general without computer vision. Biologically inspired adaptive vision models have started to outperform traditional pre-programmed methods: our fast deep / recurrent neural networks recently collected a string of 1st ranks in many important visual pattern recognition benchmarks: IJCNN traffic sign competition, NORB, CIFAR10, MNIST, three ICDAR handwriting competitions. We greatly profit from recent advances in computing hardware, complementing recent progress in the AGI theory of mathematically optimal universal problem solvers. © 2011 Springer-Verlag Berlin Heidelberg.

AB - Artificial General Intelligence will not be general without computer vision. Biologically inspired adaptive vision models have started to outperform traditional pre-programmed methods: our fast deep / recurrent neural networks recently collected a string of 1st ranks in many important visual pattern recognition benchmarks: IJCNN traffic sign competition, NORB, CIFAR10, MNIST, three ICDAR handwriting competitions. We greatly profit from recent advances in computing hardware, complementing recent progress in the AGI theory of mathematically optimal universal problem solvers. © 2011 Springer-Verlag Berlin Heidelberg.

UR - http://link.springer.com/10.1007/978-3-642-22887-2_25

UR - http://www.scopus.com/inward/record.url?scp=79961182701&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-22887-2_25

DO - 10.1007/978-3-642-22887-2_25

M3 - Conference contribution

SN - 9783642228865

SP - 243

EP - 246

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

On fast deep nets for AGI vision

Abstract

Bibliographical note

ASJC Scopus subject areas

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