Patrice Simard
Patrice Simard
Microsoft Research
Verified email at microsoft.com - Homepage
TitleCited byYear
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
40381994
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
18472003
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, ...
International conference on pattern recognition, 77-77, 1994
7841994
Efficient pattern recognition using a new transformation distance
P Simard, Y LeCun, JS Denker
Advances in neural information processing systems, 50-58, 1993
6471993
Comparison of learning algorithms for handwritten digit recognition
Y LeCun, LD Jackel, L Bottou, A Brunot, C Cortes, JS Denker, H Drucker, ...
International conference on artificial neural networks 60, 53-60, 1995
6131995
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
5441994
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, 1995
4421995
Prior knowledge in support vector kernels
B Schölkopf, P Simard, AJ Smola, V Vapnik
Advances in neural information processing systems, 640-646, 1998
4021998
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
3891998
High quality document image compression with DjVu
L Bottou, P Haffner, PG Howard, P Simard, Y Bengio, Y LeCun
3501998
Using machine learning to break visual human interaction proofs (HIPs)
K Chellapilla, PY Simard
Advances in neural information processing systems, 265-272, 2005
3032005
High performance convolutional neural networks for document processing
K Chellapilla, S Puri, P Simard
2932006
Tangent prop-a formalism for specifying selected invariances in an adaptive network
P Simard, B Victorri, Y LeCun, J Denker
Advances in neural information processing systems, 895-903, 1992
2751992
Boosting performance in neural networks
H Drucker, R Schapire, P Simard
Advances in Pattern Recognition Systems using Neural Network Technologies, 61-75, 1993
2701993
Counterfactual reasoning and learning systems: The example of computational advertising
L Bottou, J Peters, J Quiñonero-Candela, DX Charles, DM Chickering, ...
The Journal of Machine Learning Research 14 (1), 3207-3260, 2013
2412013
Designing human friendly human interaction proofs (HIPs)
K Chellapilla, K Larson, P Simard, M Czerwinski, M Czerwinski
Proceedings of the SIGCHI conference on Human factors in computing systems …, 2005
2402005
Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs).
K Chellapilla, K Larson, PY Simard, M Czerwinski
CEAS, 2005
2382005
Improving performance in neural networks using a boosting algorithm
H Drucker, R Schapire, P Simard
Advances in neural information processing systems, 42-49, 1993
2381993
Segmented layered image system
PY Simard, EL Renshaw, JR Rinker, HS Malvar
US Patent 7,120,297, 2006
2112006
Building segmentation based human-friendly human interaction proofs (HIPs)
K Chellapilla, K Larson, PY Simard, M Czerwinski
International Workshop on Human Interactive Proofs, 1-26, 2005
1892005
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