L
Lawrence D. Jackel
Researcher at Bell Labs
Publications - 104
Citations - 24161
Lawrence D. Jackel is an academic researcher from Bell Labs. The author has contributed to research in topics: Artificial neural network & Josephson effect. The author has an hindex of 41, co-authored 104 publications receiving 20212 citations. Previous affiliations of Lawrence D. Jackel include Alcatel-Lucent & AT&T.
Papers
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Journal ArticleDOI
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
Posted Content
End to End Learning for Self-Driving Cars
Mariusz Bojarski,Davide Del Testa,Daniel Dworakowski,Bernhard Firner,Beat Flepp,Prasoon Goyal,Lawrence D. Jackel,Mathew Monfort,Urs A. Muller,Jiakai Zhang,Xin Zhang,Jake Zhao,Karol Zieba +12 more
TL;DR: A convolutional neural network is trained to map raw pixels from a single front-facing camera directly to steering commands and it is argued that this will eventually lead to better performance and smaller systems.
Proceedings Article
Handwritten Digit Recognition with a Back-Propagation Network
Yann LeCun,Bernhard E. Boser,John S. Denker,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +7 more
TL;DR: Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task, and has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.
Proceedings ArticleDOI
Comparison of classifier methods: a case study in handwritten digit recognition
Léon Bottou,Corinna Cortes,Corinna Cortes,John S. Denker,John S. Denker,Harris Drucker,Harris Drucker,Isabelle Guyon,Lawrence D. Jackel,Yann LeCun,U.A. Muller,E. Sackinger,Patrice Y. Simard,Patrice Y. Simard,Vladimir Vapnik +14 more
TL;DR: This paper compares the performance of several classifier algorithms on a standard database of handwritten digits by considering not only raw accuracy, but also training time, recognition time, and memory requirements.
Comparison of learning algorithms for handwritten digit recognition
Yann LeCun,Lawrence D. Jackel,Léon Bottou,Léon Bottou,A. Brunot,Corinna Cortes,Corinna Cortes,John S. Denker,John S. Denker,Harris Drucker,Harris Drucker,Isabelle Guyon,Urs A. Muller,E. Sackinger,Patrice Y. Simard,Patrice Y. Simard,Vladimir Vapnik +16 more
TL;DR: This comparison of several learning algorithms for handwritten digits considers not only raw accuracy, but also rejection, training time, recognition time, and memory requirements.