What is recorded here?
Presented convolutional neural networks for document recognition, including LeNet-5, and an end-to-end trainable system combining recognition with segmentation and language constraints.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact four-author 1998 paper and the systems it reports. It is not a claim of the first convolutional network, sole credit for deep learning, or ownership of later CNN architectures and applications.
Read the original work
- Crossref record for Gradient-based learning applied to document recognition
- LeCun et al., Gradient-Based Learning Applied to Document Recognition
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Yann LeCun; Léon Bottou; Yoshua Bengio; Patrick Haffner
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States, Canada
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English catalogue narrative
LeCun, Bottou, Bengio and Haffner: gradient-based document recognition and LeNet-5
Presented convolutional neural networks for document recognition, including LeNet-5, and an end-to-end trainable system combining recognition with segmentation and language constraints.
Scientific scope
The record is the exact four-author 1998 paper and the systems it reports. It is not a claim of the first convolutional network, sole credit for deep learning, or ownership of later CNN architectures and applications.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Yann LeCun; Léon Bottou; Yoshua Bengio; Patrick Haffner
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.