Gradient-Based Learning Applied to Document Recognition
convolution, weight sharing and pooling, trained end to end
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EnglishTiếng Việt (abstract only)
- 18 sections
- 0 figures
- 0 equations
- 121 references
- cited by 2 here
- difficulty 3 of 5
- the publisher's page
Contents
- Nomenclature
- IINTRODUCTION
- IICONVOLUTIONAL NEURAL NETWORKS FOR ISOLATED CHARACTER RECOGNITION
- IIIRESULTS AND COMPARISON WITH OTHER METHODS
- IVMULTIMODULE SYSTEMS AND GRAPH TRANSFORMER NETWORKS
- VMULTIPLE OBJECT RECOGNITION: HOS
- VIGLOBAL TRAINING FOR GRAPH TRANSFORMER NETWORKS
- VIIMULTIPLE OBJECT RECOGNITION: SPACE DISPLACEMENT NEURAL NETWORK
- VIIIGraph Transformer Networks and Transducers
- IXAN ON-LINE HANDWRITING RECOGNITION SYSTEM
- XA CHECK READING SYSTEM
- XICONCLUSIONS
- Appendix a Preconditions for Faster Convergence
- Appendix B Stochastic Gradient Versus Batch Gradient
- Appendix C Stochastic Diagonal Levenberg–marquardt
- Acknowledgments