Patrice Simard
Patrice Simard
Microsoft Research
Verified email at - Homepage
Cited by
Cited by
Learning long-term dependencies with gradient descent is difficult
Y Bengio, P Simard, P Frasconi
IEEE transactions on neural networks 5 (2), 157-166, 1994
Best practices for convolutional neural networks applied to visual document analysis
PY Simard, D Steinkraus, JC Platt
Document Analysis and Recognition, 2003. Seventh International Conference on …, 2003
Comparison of classifier methods: a case study in handwritten digit recognition
L Bottou, C Cortes, JS Denker, H Drucker, I Guyon, LD Jackel, Y LeCun, ...
Proceedings of the 12th IAPR International Conference on Pattern Recognition …, 1994
Comparison of learning algorithms for handwritten digit recognition
Y LeCun, LD Jackel, L Bottou, A Brunot, C Cortes, J Denker, H Drucker, ...
International conference on artificial neural networks 60, 53-60, 1995
Efficient pattern recognition using a new transformation distance
P Simard, Y LeCun, JS Denker
Advances in neural information processing systems, 50-50, 1993
Time is of the essence: a conjecture that hemispheric specialization arises from interhemispheric conduction delay
JL Ringo, RW Doty, S Demeter, PY Simard
Cerebral Cortex 4 (4), 331-343, 1994
Learning algorithms for classification: A comparison on handwritten digit recognition
Y LeCun, LD Jackel, L Bottou, C Cortes, JS Denker, H Drucker, I Guyon, ...
Neural networks: the statistical mechanics perspective 261 (276), 2, 1995
High performance convolutional neural networks for document processing
K Chellapilla, S Puri, P Simard
Tenth international workshop on frontiers in handwriting recognition, 2006
Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising.
L Bottou, J Peters, J Quiñonero-Candela, DX Charles, DM Chickering, ...
Journal of Machine Learning Research 14 (11), 2013
Transformation invariance in pattern recognition—tangent distance and tangent propagation
PY Simard, YA LeCun, JS Denker, B Victorri
Neural networks: tricks of the trade, 239-274, 1998
Prior knowledge in support vector kernels
B Schölkopf, P Simard, AJ Smola, V Vapnik
Advances in neural information processing systems, 640-646, 1998
Using machine learning to break visual human interaction proofs (HIPs)
K Chellapilla, PY Simard
Advances in neural information processing systems 17, 265-272, 2005
High quality document image compression with
L Bottou, P Haffner, PG Howard, P Simard, Y Bengio, Y Le Cun
Journal of Electronic Imaging 7 (3), 410-425, 1998
Tangent prop-a formalism for specifying selected invariances in an adaptive network
P Simard, B Victorri, Y LeCun, JS Denker
NIPS 91, 895-903, 1991
Boosting performance in neural networks
H Drucker, R Schapire, P Simard
Advances in Pattern Recognition Systems using Neural Network Technologies, 61-75, 1993
Designing human friendly human interaction proofs (HIPs)
K Chellapilla, K Larson, P Simard, M Czerwinski
Proceedings of the SIGCHI conference on Human factors in computing systems …, 2005
Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs).
K Chellapilla, K Larson, PY Simard, M Czerwinski
CEAS, 2005
Improving performance in neural networks using a boosting algorithm
H Drucker, R Schapire, P Simard
Advances in neural information processing systems, 42-49, 1993
Using GPUs for machine learning algorithms
D Steinkraus, I Buck, PY Simard
Eighth International Conference on Document Analysis and Recognition (ICDAR …, 2005
The problem of learning long-term dependencies in recurrent networks
Y Bengio, P Frasconi, P Simard
IEEE international conference on neural networks, 1183-1188, 1993
The system can't perform the operation now. Try again later.
Articles 1–20