Patrice Y. Simard spends much of his time researching Artificial intelligence, Pattern recognition, Digit recognition, Training set and Speech recognition. His Artificial intelligence study frequently links to related topics such as Machine learning. His Pattern recognition study integrates concerns from other disciplines, such as NIST and Algorithm.
His work in Digit recognition covers topics such as Intelligent word recognition which are related to areas like Training time. His work in Training set addresses subjects such as Handwriting recognition, which are connected to disciplines such as Classifier and MNIST database. His Speech recognition research incorporates elements of Optical character recognition and Probably approximately correct learning.
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Artificial neural network. Classifier, Segmentation, Convolutional neural network, Optical character recognition and Intelligent word recognition are the subjects of his Artificial intelligence studies. His Pattern recognition research integrates issues from NIST, Speech recognition, Character recognition and Digit recognition.
His research investigates the connection between Speech recognition and topics such as Handwriting recognition that intersect with problems in Handwriting. Much of his study explores Machine learning relationship to Pattern recognition. His Artificial neural network research is multidisciplinary, incorporating elements of Graphics, Graphics processing unit and Word error rate.
Patrice Y. Simard focuses on Artificial intelligence, Machine learning, Interactive Learning, Segmentation and Web page. His Artificial intelligence study frequently involves adjacent topics like Natural language processing. His biological study spans a wide range of topics, including Speech recognition and Feature.
In general Machine learning, his work in Leverage is often linked to Counterfactual thinking linking many areas of study. Patrice Y. Simard combines subjects such as Regularization and Relevance with his study of Segmentation. His Training set study necessitates a more in-depth grasp of Pattern recognition.
Patrice Y. Simard mostly deals with Machine learning, Artificial intelligence, Causal inference, Causation and Search engine. His Algorithmic learning theory and Feature study in the realm of Machine learning interacts with subjects such as USable, Analysis tools and Data type. The various areas that Patrice Y. Simard examines in his Algorithmic learning theory study include Instance-based learning and Computational learning theory.
USable is integrated with Regularization, Cold start, Interactive Learning, Relevance and Web page in his research. He combines topics linked to Segmentation with his work on Regularization. His Causal inference research covers fields of interest such as Computational advertising and Counterfactual thinking.
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Learning long-term dependencies with gradient descent is difficult
Y. Bengio;P. Simard;P. Frasconi.
IEEE Transactions on Neural Networks (1994)
Best practices for convolutional neural networks applied to visual document analysis
P.Y. Simard;D. Steinkraus;J.C. Platt.
international conference on document analysis and recognition (2003)
Comparison of classifier methods: a case study in handwritten digit recognition
L. Bottou;C. Cortes;C. Cortes;J.S. Denker;J.S. Denker;H. Drucker;H. Drucker.
international conference on pattern recognition (1994)
Learning algorithms for classification: A comparison on handwritten digit recognition
Yann Lecun;L.D. Jackel;Leon Bottou;Leon Bottou;Corinna Cortes;Corinna Cortes.
Comparison of learning algorithms for handwritten digit recognition
Yann Lecun;L.D. Jackel;Leon Bottou;Leon Bottou;A. Brunot.
Efficient Pattern Recognition Using a New Transformation Distance
Patrice Simard;Patrice Simard;Yann LeCun;John S. Denker;John S. Denker.
neural information processing systems (1992)
Time Is of the Essence: A Conjecture that Hemispheric Specialization Arises from Interhemispheric Conduction Delay
James L. Ringo;Robert W. Doty;Steven Demeter;Patrice Y. Simard.
Cerebral Cortex (1994)
High Performance Convolutional Neural Networks for Document Processing
Kumar Chellapilla;Sidd Puri;Patrice Simard.
international conference on frontiers in handwriting recognition (2006)
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Patrice Simard;Yann LeCun;John S. Denker;Bernard Victorri.
neural information processing systems (1998)
Counterfactual reasoning and learning systems: the example of computational advertising
Léon Bottou;Jonas Peters;Joaquin Quiñonero-Candela;Denis X. Charles.
Journal of Machine Learning Research (2013)
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