On fast deep nets for AGI vision

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 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 languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages243-246
Number of pages4
DOIs
StatePublished - Aug 11 2011
Externally publishedYes

Bibliographical note

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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