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- Yann LeCun, Bernhard E. Boser, +4 authors Lawrence D. Jackel
- Neural Computation
- 1989

The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into aâ€¦ (More)

- Yann LeCun, Bernhard E. Boser, +4 authors Lawrence D. Jackel
- NIPS
- 1989

We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained andâ€¦ (More)

- Yann LeCun, John S. Denker, Sara A. Solla
- NIPS
- 1989

We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, severalâ€¦ (More)

- Patrice Y. Simard, Yann LeCun, John S. Denker, Bernard Victorri
- Neural Networks: Tricks of the Trade
- 1996

In pattern recognition, statistical modeling, or regression, the amount of data is the most critical factor a ecting the performance. If the amount of data and computational resources are near inâ€¦ (More)

- Patrice Y. Simard, Yann LeCun, John S. Denker
- NIPS
- 1992

Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple distances (Euclidean, dot product ... ), which are not particularly meaningful onâ€¦ (More)

This paper compares the performance of several classi er algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, andâ€¦ (More)

Bernard Victorri Universite de Caen Caen 14032 Cedex France John Denker AT&T Bell Laboratories 101 Crawford Corner Rd Holmdel, NJ 07733 In many machine learning applications, one has access, not onlyâ€¦ (More)

- Alan Kramer, John S. Denker, B. Flower, J. Moroney
- ISLPD
- 1995

Recent advances in compact, practical adiabatic computing circuits which demonstrate signi cant energy savings have renewed interest in using such techniques in lowpower systems. Several recentlyâ€¦ (More)

We present a feed-forward network architecture for recognizing an unconstrained handwritten multi-digit string. This is an extension of previous work on recognizing isolated digits. In thisâ€¦ (More)

We have designed a writer-adaptive character recognition system for on-line characters entered on a touch-terminal. It is based on a Time Delay Neural Network (TDNN) that is rst trained on examplesâ€¦ (More)