Yann LeCun's Publications
  

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Selected List of Representative Papers

[Farabet et al. 2013]: Learning Hierarchical Features for Scene Labeling, scheduled to appear in the special issue on deep learning of IEEE Trans. on Pattern Analysis and Machine Intelligence. The task is to label all the pixels in an image with the category of the object it belongs to. This is sometimes called scene labeling, scene parsing, or semantic segmentation. The bottom line is that our system beat all previously published scene labeling systems on accuracy on three standard datasets: Stanford Bakground (8 classes), SIFTflow (33 classes) and Barcelona (170 classes). It alsod beat the best competitors by a factor of 100 in speed. Our system is a multiscale convolutional network trained in purely supervised mode (with backprop) to label each pixel. The decisions are then cleaned up by a simple post-processing (the simplest one consisting in taking the majority category within a superpixel).

[Hadsell et al. 2009]: Learning Long-Range Vision for Autonomous Off-Road Driving, and a companion paper [Sermanet et al. 2009]:A Multi-Range Architecture for Collision-Free Off-Road Robot Navigation both scheduled to appear in the Journal of Field Robotics: These two papers describe (in excruciating details) our work on the DARPA LAGR project. We developed a learning-based long-range vision system that can detect obstacles and pathways at very long range, using a combination of training from log files in the lab and on-line adaptation as the robot runs. The robot uses labels obtained from stereo vision to train its monocular long-range obstacle classifier. The system also uses learning for it dynamical trajectory control. Further information is available here.

[LeCun et al. 2006]: A Tutorial on Energy-Based Learning (in Bakir et al. (eds) "Predicting Strutured Data", MIT Press 2006): This is a tutorial paper on Energy-Based Models (EBM). Inference in EBMs consists in searching for the value of the output variables that minimize an energy function. Learning consists in shaping that energy function in such a way that desired configuration have lower energy than undesired ones. The EBM approach provides a common theoretical framework for many probabilistic and non-probabilistic learning models, including traditional discriminative and generative approaches, as well as graph-transformer networks, conditional random fields, maximum margin Markov networks, and several manifold learning methods. Some of the methods described in this paper help circumvent the problem of evaluating partition functions that often plagues probabilistic methods. Further information is available here.

[Bengio, LeCun 2007]: Scaling Learning Algorithms Towards AI: (in Bottou et al. (Eds) "Large-Scale Kernel Machines", MIT Press 2007). We present theoretical and empirical evidence showing that kernel methods and other "shallow" architectures are inefficient for representing complex functions such as the ones involved in artificially intelligent behavior, such as visual perception. We argue that "deep" architectures are not subject to the same limitations and review recent advances in learning algorithms for deep architectures.

[Mirowski et al., 2008]: Comparing SVM and Convolutional Networks for Epileptic Seizure Prediction from Intracranial EEG (MLSP 2008): We show that epilepsy seizures can be predicted about one hour in advance, with essentially no false positives, using signals from intracranial electrodes. A number of different pairwise features that measure the synchrony between pairs of electrodes over 5-second time segments were used. Temporal Convolutional Networks and Support Vector Machines fed with 1-minute sequences of feature vectors were tested the Freiburg dataset. The convolutional network was shown to detect all seizures about 1 hour in advance with no false alarm for all patients in the dataset, significantly outperforming the SVM.

[Chopra et al., 2007]: Discovering the hidden structure of house prices with non-parametric latent manifold model (KDD 2007): In many regression problems, the variable to be predicted depends not only on a sample-specific feature vector, but also on an unknown (latent) manifold that must satisfy known constraints. An example is house prices which depend on the characteristics of the house, and on the desirability of the neighborhood, which is not directly measurable. The proposed method comprises two trainable components. The first one is a parametric model that predicts the "intrinsic" price the house from its description. The second one is a smooth, non-parametric model of the latent "desirability" manifold. The predicted price of a house is the product intrinsic price and desirability. The two components are trained simultanesously using a deterministic form of the EM algorithm. The model was trained on a large dataset of house prices from Los Angeles county. It produces better predictions than pure parametric and non-parametric models. It also produces useful estimates of the desirability surface at each location.

[LeCun, Huang, and Bottou, 2004]: Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting (CVPR 2004): Generic object detection and recognition using convolutional nets. The system can detect and recognize cars, truck, airplanes, human figures, and 4-legged animals in cluttered scenes in real time, with invariance to pose, illumination and clutter. Further information is available here.

[Osadchy, Miller and LeCun, 2007] and [Osadchy, Miller and LeCun, 2005]: Synergistic Face Detection and Pose Estimation with Energy-Based Model (NIPS 2004, JMLR 2007): real-time simultaneous face detection and pose estimation with convolutional networks trained to produce points on a "face manifold" using an Energy-Based loss function. Further information is available here. The method is a direct descendent of the first learning-based system for face detection [Vaillant, Monrocq, and LeCun 1994]: Original approach for the localisation of objects in images, IEE Proc on Vision, Image, and Signal Processing (1994), which followed a paper with the same title published at ICANN 1993 (these predate [Rowley, Baluja, Kanade 1997] and [Viola and Jones 2001]).

[Hadsell, Chopra and LeCun, 2006]: Dimensionality Reduction by Learning an Invariant Mapping (CVPR 2006): Mapping image to a low dimensional representation with invariance to illumination or other factors.

[Chopra and Hadsell and LeCun, 2005]: Learning a Similarity Metric Discriminatively, with Application to Face Verification (CVPR 2005): Using convolutional nets and Energy-Based Models to learn an invariant similarity metric between images of faces.

[LeCun et al., 2005]: Off-Road Obstacle Avoidance through End-to-End Learning (NIPS 2005): Training a robot to avoid obstacles by watching over the shoulder of a human driver. No preprocessing necessary. Further information and videos are available here

[Ning et al., 2005]: Toward Automatic Phenotyping of Developing Embryos from Videos (IEEE Trans. Image Processing, 2005): Using convolutional nets and Energy-Based Models to segment and locate the cells and nuclei in videos of developing embryos of C. Elegans roundworms.

[LeCun et al., 1998]: Gradient-Based Learning Applied to Document Recognition (Proc. IEEE 1998): A long and detailed paper on convolutional nets, graph transformer networks, and discriminative training methods for sequence labeling. We show how to build systems that integrate segmentation, feature extraction, classification, contextual post-processing, and language modeling into one single learning machine trained end-to-end. Applications to handwriting recognition and face detection are described.

[Simard et al., 2000]: Transformation Invariance in Pattern Recognition: Our latest and most complete paper on Tangent Distance, a method for making distance-based classifiers (nearest neighbor, SVM,...) locally invariant to a set of known transformation, and Tangent Propagation, a method for training learning machines to be locally invariant to a set of transformations.

[LeCun et al., 1998]: Efficient BackProp: all the tricks and the theory behind them to efficiently train neural networks with backpropagation, including how to compute the optimal learning rate, how to back-propagate second derivatives, and other sundries.

[Bottou et al., 1998]: High Quality Document Image Compression with DjVu: our first paper on DjVu, still the best method for compressing and distributing scanned documents and high-resolution images. the results are a bit dated, but the more recent papers are less all-encompassing.

[LeCun, 1988]: A theoretical framework for Back-Propagation: how backprop for feed-forward and recurrent nets can be derived cleanly from a Lagrangian formalism.

[LeCun, Denker, and Solla, 1990]: Optimal Brain Damage: a simple and effective "pruning" technique to remove extraneous parameters in learning machines.

[Simard and LeCun, 1992] Reverse TDNN: an architecture for trajectory generation: how a convolutional net turned on its head can be used to synthesize signals or images, instead of recognizing them.

[LeCun and Kanter and Solla, 1991]: Eigenvalues of covariance matrices: application to neural-network learning: why learning is optimal when the number of training samples is about 4 times the number of parameters.

All Publications (in reverse chronological order)

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179. Camille Couprie, Clement Farabet, Yann LeCun and Laurent Najman: Causal Graph-Based Video Segmentation, Proc. International Conference on Image Processing (ICIP'13), IEEE, September 2013, \cite{couprie-icip-13}. 509KBDjVu
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178. Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala and Yann LeCun: Pedestrian Detection with Unsupervised Multi-Stage Feature Learning, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR'13), IEEE, June 2013, \cite{sermanet-cvpr-13}. 208KBDjVu
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177. Tom Schaul, Sixin Zhang and Yann LeCun: No more Pesky Learning Rates, Proc. International Conference on Machine learning (ICML'13), 2013, \cite{schaul-icml-13}. 738KBDjVu
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176. Li Wan, Matthew Zeiler, Sixin Zhang, Yann LeCun and Rob Fergus: Regularization of Neural Networks using DropConnect, Proc. International Conference on Machine learning (ICML'13), 2013, \cite{wan-icml-13}. 396KBDjVu
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175. Jason Tyler Rolfe and Yann LeCun: Discriminative Recurrent Sparse Auto-Encoders, International Conference on Learning Representations (ICLR2013), April 2013, \cite{rolfe-lecun-iclr-13}. 368KBDjVu
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174. Rotislav Goroshin and Yann LeCun: Saturating Auto-Encoders, International Conference on Learning Representations (ICLR2013), April 2013, \cite{goroshin-lecun-iclr-13}. 229KBDjVu
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173. Camille Couprie, Clement Farabet, Laurent Najman and Yann LeCun: Indoor Semantic Segmentation using Depth Information, International Conference on Learning Representations (ICLR2013), April 2013, \cite{couprie-iclr-13}. 393KBDjVu
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172. Clement Farabet, Camille Couprie, Laurent Najman and Yann LeCun: Learning Hierarchical Features for Scene Labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, in press, August 2013, \cite{farabet-pami-13}. 1144KBDjVu
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171. Eric J. Humphrey, Juan Pablo Bello and Yann LeCun: Moving beyond feature design: Deep architectures and automatic feature learning in music informatics, Proceedings of International Symposium on Music Information Retrieval (ISMIR'12), 2012, \cite{humphrey-ismir-12}. 368KBDjVu
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170. Pierre Sermanet, Soumith Chintala and Yann LeCun: Convolutional Neural Networks Applied to House Numbers Digit Classification, International Conference on Pattern Recognition (ICPR 2012), 2012, \cite{sermanet-icpr-12}. 234KBDjVu
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169. Yann LeCun: Learning Invariant Feature Hierarchies, in Fusiello, Andrea and Murino, Vittorio and Cucchiara, Rita (Eds), European Conference on Computer Vision (ECCV 2012), 7583:496-505, Lecture Notes in Computer Science, Springer, ISBN:978-3-642-33862-5, Workshop on Biological and Computer Vision Interfaces (invited paper), 2012, \cite{lecun-eccv-12}. 284KBDjVu
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168. Arthur Szlam, Karol Gregor and Yann LeCun: Fast Approximations to Structured Sparse Coding and Applications to Object Classification, in Fitzgibbon, Andrew and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia (Eds), European Conference on Computer Vision (ECCV 2012), 7576:200-213, Lecture Notes in Computer Science, Springer, ISBN:978-3-642-33714-7, 2012, \cite{szlam-gregor-lecun-eccv-12}. 302KBDjVu
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167. Jose M. Alvarez, Theo Gevers, Yann LeCun and Antonio M. Lopez: Road Scene Segmentation from a Single Image, in Fitzgibbon, Andrew and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia (Eds), European Conference on Computer Vision (ECCV 2012), 7578:376-389, Lecture Notes in Computer Science, Springer, ISBN:978-3-642-33785-7, 2012, \cite{alvarez-eccv-12}. 736KBDjVu
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166. Jose M. Alvarez, Yann LeCun, Theo Gevers and Antonio M. Lopez: Semantic Road Segmentation via Multi-scale Ensembles of Learned Features, in Fusiello, Andrea and Murino, Vittorio and Cucchiara, Rita (Eds), European Conference on Computer Vision (ECCV 2012), 7584:586-595, Lecture Notes in Computer Science, Springer, ISBN:978-3-642-33867-0, Workshop on Computer Vision in Vehicle Technology: From Earth to Mars, 2012, \cite{alvarez-eccv-12b}. 595KBDjVu
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165. P. Mirowski and Y. LeCun: Statistical Machine Learning and Dissolved Gas Analysis: A Review, IEEE Transactions on Power Delivery, 27(4):1791-1799, october 2012, \cite{mirowski-lecun-12}. 357KBDjVu
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164. Phi-Hung Pham, Darko Jelaca, Clement Farabet, Berin Martini, Yann LeCun and Eugenio Culurciello: NeuFlow: Dataflow Vision Processing System-on-a-Chip, Proc. International Midwest Symposium on Circuits and Systems (MWSCAS'12), IEEE, invited paper, 2012, \cite{pham-mwscas-12}. 514KBDjVu
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163. Clement Farabet, Camille Couprie, Laurent Najman and Yann LeCun: Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, Proc. International Conference on Machine learning (ICML'12), 2012, \cite{farabet-icml-12}. 1009KBDjVu
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162. Clement Farabet, Rafael Paz, Jose Perez-Carrasco, Carlos Zamarreno, Alejandro Linares-Barranco, Yann LeCun, Eugenio Culurciello, Teresa Serrano-Gotarredona and Bernabe Linares-Barranco: Comparison Between Frame-Constrained Fix-Pixel-Value and Frame-Free Spiking-Dynamic-Pixel ConvNets for Visual Processing, Frontiers in Neuroscience, 6(00032), DOI: 10.3389/fnins.2012.00032 (open access), 2012, \cite{farabet-frontiersin-12}. 939KBDjVu
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161. Tapani Raiko, Harri Valpola and Yann LeCun: Deep Learning Made Easier by Linear Transformations in Perceptrons, Conference on AI and Statistics (JMLR W&CP), 22:924-932, (JMLR link), 2012, \cite{raiko-aistats-12}. 274KBDjVu
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160. Karol Gregor, Arthur Szlam and Yann LeCun: Structured Sparse Coding via Lateral Inhibition, Advances in Neural Information Processing Systems (NIPS 2011), 24, 2011, \cite{gregor-nips-11}. 321KBDjVu
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159. Mikael Henaff, Kevin Jarrett, Koray Kavukcuoglu and Yann LeCun: Unsupervised Learning of Sparse Features for Scalable Audio Classification, Proceedings of International Symposium on Music Information Retrieval (ISMIR'11), (Best Student Paper Award), 2011, \cite{henaff-ismir-11}. 147KBDjVu
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158. Y-Lan Boureau, Nicolas Le Roux, Francis Bach, Jean Ponce and Yann LeCun: Ask the locals: multi-way local pooling for image recognition, Proc. International Conference on Computer Vision (ICCV'11), 2011, \cite{boureau-iccv-11}. 130KBDjVu
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157. Clement Farabet, Yann LeCun, Koray Kavukcuoglu, Eugenio Culurciello, Berin Martini, Polina Akselrod and Selcuk Talay: Large-Scale FPGA-based Convolutional Networks, in Bekkerman, Ron and Bilenko, Mikhail and Langford, John (Eds), Scaling up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, 2011, \cite{farabet-suml-11}. 235KBDjVu
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156. Pierre Sermanet and Yann LeCun: Traffic Sign Recognition with Multi-Scale Convolutional Networks, Proceedings of International Joint Conference on Neural Networks (IJCNN'11), 2011, \cite{sermanet-ijcnn-11}. 363KBDjVu
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155. Clément Farabet, Berin Martini, Benoit Corda, Polina Akselrod, Eugenio Culurciello and Yann LeCun: NeuFlow: A Runtime-Reconfigurable Dataflow Processor for Vision, Proceedings of Embedded Computer Vision Workshop (ECVW'11), (invited paper), 2011, \cite{farabet-ecvw-11}. 239KBDjVu
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154. Gabriel Krouk, Piotr Mirowski, Yann LeCun, Dennis Shasha and Gloria Coruzzi: Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate, Genome Biology, 11(R123), DOI (open access), December 2010, \cite{krouk-gb-10}. 897KBDjVu
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153. Piotr Mirowski, Marc'Aurelio Ranzato and Yann LeCun: Dynamic Auto-Encoders for Semantic Indexing, Proceedings of the NIPS 2010 Workshop on Deep Learning, 2010, \cite{mirowski-nipsdl-10}. 321KBDjVu
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152. Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Michaël Mathieu and Yann LeCun: Learning Convolutional Feature Hierachies for Visual Recognition, Advances in Neural Information Processing Systems (NIPS 2010), 23, 2010, \cite{koray-nips-10}. 247KBDjVu
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151. Durk Kingma and Yann LeCun: Regularized estimation of image statistics by score matching, Advances in Neural Information Processing Systems (NIPS 2010), 23, (including supplemental material), 2010, \cite{kingma-nips-10}. 600KBDjVu
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150. Matthew Koichi Grimes, Dragomir Anguelov and Yann LeCun: Hybrid Hessians for Flexible Optimization of Pose Graphs, Proc. International Conference on Intelligent Robots and Systems (IROS), 2010, \cite{grimes-iros-10}. 691KBDjVu
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149. W. Taylor, Graham, Rob Fergus, Yann LeCun and Christoph Bregler: Convolutional Learning of Spatio-temporal Features, Proc. European Conference on Computer Vision (ECCV'10), 2010, \cite{taylor-eccv-10}. 467KBDjVu
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148. Karol Gregor and Yann LeCun: Learning Fast Approximations of Sparse Coding, Proc. International Conference on Machine learning (ICML'10), 2010, \cite{gregor-icml-10}. 178KBDjVu
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147. Y-Lan Boureau, Jean Ponce and Yann LeCun: A theoretical analysis of feature pooling in vision algorithms, Proc. International Conference on Machine learning (ICML'10), 2010, \cite{boureau-icml-10}. 189KBDjVu
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146. Y-Lan Boureau, Francis Bach, Yann LeCun and Jean Ponce: Learning Mid-Level Features for Recognition, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR'10), IEEE, 2010, \cite{boureau-cvpr-10}. 178KBDjVu
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145. Clément Farabet, Berin Martini, Polina Akselrod, Selçuk Talay, Yann LeCun and Eugenio Culurciello: Hardware Accelerated Convolutional Neural Networks for Synthetic Vision Systems, Proc. International Symposium on Circuits and Systems (ISCAS'10), IEEE, 2010, \cite{farabet-iscas-10}. 153KBDjVu
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144. Yann LeCun, Koray Kavukvuoglu and Clément Farabet: Convolutional Networks and Applications in Vision, Proc. International Symposium on Circuits and Systems (ISCAS'10), IEEE, 2010, \cite{lecun-iscas-10}. 189KBDjVu
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143. Clément Farabet, Cyril Poulet and Yann LeCun: An FPGA-Based Stream Processor for Embedded Real-Time Vision with Convolutional Networks, Fifth IEEE Workshop on Embedded Computer Vision (ECV'09), IEEE, Kyoto, October 2009, \cite{farabet-ecv-09}. 326KBDjVu
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142. Pierre Sermanet, Koray Kavukcuoglu and Yann LeCun: EBLearn: Open-Source Energy-Based Learning in C++, Proc. International Conference on Tools with Artificial Intelligence (ICTAI'09), IEEE, 2009, \cite{sermanet-ictai-09}. 302KBDjVu
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141. Piotr Mirowski, Deepak Madhavan, Yann LeCun and Ruben Kuzniecky: Classification of Patterns of EEG Synchronization for Seizure Prediction, Clinical Neurophysiology, 120(11):1927-1940, DOI, November 2009, \cite{mirowski-cneuro-09}. 1300KBDjVu
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140. Kevin Jarrett, Koray Kavukcuoglu, Marc'Aurelio Ranzato and Yann LeCun: What is the Best Multi-Stage Architecture for Object Recognition?, Proc. International Conference on Computer Vision (ICCV'09), IEEE, 2009, \cite{jarrett-iccv-09}. 303KBDjVu
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139. Piotr Mirowski and Yann LeCun: Dynamic Factor Graphs for Time Series Modeling, in Buntine, Wray and Grobelnik, Marko and Mladenic, Dunja and Shawe-Taylor, John (Eds), Machine Learning and Knowledge Discovery in Databases (ECML/PKDD'09), 5782:128-143, Springer, ISBN:978-3-642-04173-0, 2009, \cite{mirowski-ecml-09}. 488KBDjVu
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138. Clément Farabet, Cyril poulet, Jefferson Y. Han and Yann LeCun: CNP: An FPGA-based Processor for Convolutional Networks, International Conference on Field Programmable Logic and Applications, IEEE, Prague, September 2009, \cite{farabet-fpl-09}. 240KBDjVu
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137. Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergus and Yann LeCun: Learning Invariant Features through Topographic Filter Maps, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR'09), IEEE, 2009, \cite{koray-cvpr-09}. 334KBDjVu
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136. Matthew Grimes and Yann LeCun: Efficient Off-Road Localization Using Visually Corrected Odometry, Proc. International Conference on Robotics and Automation (ICRA'09), IEEE, 2009, \cite{grimes-icra-09}. 1548KBDjVu
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135. Raia Hadsell, Pierre Sermanet, Marco Scoffier, Ayse Erkan, Koray Kavackuoglu, Urs Muller and Yann LeCun: Learning Long-Range Vision for Autonomous Off-Road Driving, Journal of Field Robotics, 26(2):120-144, February 2009, \cite{hadsell-jfr-09}. 1330KBDjVu
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134. Pierre Sermanet, Raia Hadsell, Marco Scoffier, Matt Grimes, Jan Ben, Ayse Erkan, Chris Crudele, Urs Muller and Yann LeCun: A Multi-Range Architecture for Collision-Free Off-Road Robot Navigation, Journal of Field Robotics, 26(1):58-87, January 2009, \cite{sermanet-jfr-09}. 5401KBDjVu
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133. Andrew Caplin, Sumit Chopra, John Leahy, Yann LeCun and Trivikrmaman Thampy: Machine Learning and the Spatial Structure of House Prices and Housing Returns, Social Science Research Network, SSRN ID: 1316046, Available at SSRN: http://ssrn.com/abstract=1316046, December 2008, \cite{caplin-ssrn-08}. 324KBDjVu
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132. Pierre Sermanet, Marco Scoffier, Chris Crudele, Urs Muller and Yann LeCun: Learning Maneuver Dictionaries for Ground Robot Planning, Proc. 39th International Symposium on Robotics (ISR'08), 2008, \cite{sermanet-isr-08}. 1990KBDjVu
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131. Piotr Mirowski, Yann LeCun, Deepak Madhavan and Ruben Kuzniecky: Comparing SVM and Convolutional Networks for Epileptic Seizure Prediction from Intracranial EEG, Proc. Machine Learning and Signal Processing (MLSP'08), IEEE, 2008, \cite{mirowski-mlsp-08}. 266KBDjVu
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130. Raia Hadsell, Ayse Erkan, Pierre Sermanet, Marco Scoffier, Urs Muller and Yann LeCun: Deep Belief Net Learning in a Long-Range Vision System for Autonomous Off-Road Driving, Proc. Intelligent Robots and Systems (IROS'08), 2008, \cite{hadsell-iros-08}. 517KBDjVu
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129. Pierre Sermanet, Raia Hadsell, Marco Scoffier, Urs Muller and Yann LeCun: Mapping and Planning under Uncertainty in Mobile Robots with Long-Range Perception, Proc. Intelligent Robots and Systems (IROS'08), 2008, \cite{sermanet-iros-08}. 205KBDjVu
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128. Marc'Aurelio Ranzato, Y-Lan Boureau and Yann LeCun: Sparse feature learning for deep belief networks, Advances in Neural Information Processing Systems (NIPS 2007), 20, 2007, \cite{ranzato-nips-07}. 129KBDjVu
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127. Marc'Aurelio Ranzato and Yann LeCun: A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images, Proc. International Conference on Document Analysis and Recognition (ICDAR), 2007, \cite{ranzato-icdar-07}. 139KBDjVu
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126. Ayse Erkan, Raia Hadsell, Pierre Sermanet, Jan Ben, Urs Muller and Yann LeCun: Adaptive Long Range Vision in Unstructured Terrain, Proc. Intelligent Robots and Systems (IROS'07), 2007, \cite{erkan-iros-07}. 211KBDjVu
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125. Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato and Fu-Jie Huang: Energy-Based Models in Document Recognition and Computer Vision, Proc. International Conference on Document Analysis and Recognition (ICDAR), (keynote address), 2007, \cite{lecun-icdar-keynote-07}. 110KBDjVu
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124. Raia Hadsell, Ayse Erkan, Pierre Sermanet, Jan Ben, Koray Kavukcuoglu, Urs Muller and Yann LeCun: A Multi-Range Vision Strategy for Autonomous Offroad Navigation, Proc. Robotics and Applications (RA'07), 2007, \cite{hadsell-ra-07}. 416KBDjVu
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123. Sumit Chopra, Trivikraman Thampy, John Leahy, Andrew Caplin and Yann LeCun: Discovering the hidden structure of house prices with non-parametric latent manifold model, Proc. Knowledge Discovery in Databases (KDD'07), 2007, \cite{chopra-kdd-07}. 422KBDjVu
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122. Pierre Sermanet, Raia Hadsell, Jan Ben, Ayse Naz Erkan, Beat Flepp, Urs Muller and Yann LeCun: Speed-Range Dilemmas for Vision-Based Navigation in Unstructured Terrain, Proc. 6th IFAC Symposium on Intelligent Autonomous Vehicles, 2007, \cite{sermanet-07}. 149KBDjVu
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121. M. Osadchy, Y. LeCun and M. Miller: Synergistic Face Detection and Pose Estimation with Energy-Based Models, Journal of Machine Learning Research, 8:1197-1215, May 2007, \cite{osadchy-07}. 382KBDjVu
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120. Raia Hadsell, Pierre Sermanet, Ayse Erkan, Jan Ben, Jeff Han, Beat Flepp, Urs Muller and Yann LeCun: On-line Learning for Offroad Robots: Using Spatial Label Propagation to Learn Long-Range traversability, Proc. Robotics Science and Systems 07, 2007, \cite{hadsell-rss-07}. 359KBDjVu
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119. Marc'Aurelio Ranzato, Fu-Jie Huang, Y-Lan Boureau and Yann LeCun: Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition, Proc. Computer Vision and Pattern Recognition Conference (CVPR'07), IEEE Press, 2007, \cite{ranzato-cvpr-07}. 187KBDjVu
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118. Marc'Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra and Yann LeCun: A Unified Energy-Based Framework for Unsupervised Learning, Proc. Conference on AI and Statistics (AI-Stats), 2007, \cite{ranzato-unsup-07}. 257KBDjVu
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117. Yoshua Bengio and Yann LeCun: Scaling learning algorithms towards AI, in Bottou, L. and Chapelle, O. and DeCoste, D. and Weston, J. (Eds), Large-Scale Kernel Machines, MIT Press, 2007, \cite{bengio-lecun-07}. 427KBDjVu
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116. M. Ranzato, P.E. Taylor, J.M. House, R.C. Flagan, Y. LeCun and P. Perona: Automatic recognition of biological particles in microscopic images, Pattern Recognition Letters, 28(1):31-39, January 2007, \cite{ranzato-07a}. 273KBDjVu
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115. Marc'Aurelio Ranzato, Christopher Poultney, Sumit Chopra and Yann LeCun: Efficient Learning of Sparse Representations with an Energy-Based Model, in J. Platt et al. (Eds), Advances in Neural Information Processing Systems (NIPS 2006), 19, MIT Press, 2006, \cite{ranzato-06}. 152KBDjVu
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114. Yann LeCun, Sumit Chopra, Raia Hadsell, Marc'Aurelio Ranzato and Fu-Jie Huang: A Tutorial on Energy-Based Learning, in Bakir, G. and Hofman, T. and Schölkopf, B. and Smola, A. and Taskar, B. (Eds), Predicting Structured Data, MIT Press, 2006, \cite{lecun-06}. 677KBDjVu
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113. Raia Hadsell, Sumit Chopra and Yann LeCun: Dimensionality Reduction by Learning an Invariant Mapping, Proc. Computer Vision and Pattern Recognition Conference (CVPR'06), IEEE Press, 2006, \cite{hadsell-chopra-lecun-06}. 480KBDjVu
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112. Fu-Jie Huang and Yann LeCun: Large-Scale Learning with SVM and Convolutional Nets for Generic Object Categorization, Proc. Computer Vision and Pattern Recognition Conference (CVPR'06), IEEE Press, 2006, \cite{huang-lecun-06}. 154KBDjVu
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111. Y. LeCun, U. Muller, J. Ben, E. Cosatto and B. Flepp: Off-Road Obstacle Avoidance through End-to-End Learning, in Y. Weiss, B. Scholkopf, and J. Platt (Eds), Advances in Neural Information Processing Systems (NIPS 2005), 18, MIT Press, 2005, \cite{lecun-dave-05}. 161KBDjVu
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110. Feng Ning, Damien Delhomme, Yann LeCun, Fabio Piano, Leon Bottou and Paolo Barbano: Toward Automatic Phenotyping of Developing Embryos from Videos, IEEE Transactions on Image Processing, 14(9):1360-1371, Special issue on Molecular and Cellular Bioimaging, September 2005, \cite{ning-05}. 669KBDjVu
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109. Sumit Chopra, Raia Hadsell and Yann LeCun: Learning a Similarity Metric Discriminatively, with Application to Face Verification, Proc. of Computer Vision and Pattern Recognition Conference, IEEE Press, 2005, \cite{chopra-05}. 157KBDjVu
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108. Yann LeCun and Fu Jie Huang: Loss Functions for Discriminative Training of Energy-Based Models, Proc. of the 10-th International Workshop on Artificial Intelligence and Statistics (AIStats'05) , 2005, \cite{lecun-huang-05}. 99KBDjVu
344KBPDF
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107. R. Osadchy, M. Miller and Y. LeCun: Synergistic Face Detection and Pose Estimation with Energy-Based Model, Advances in Neural Information Processing Systems (NIPS 2004), 17, MIT Press, 2005, \cite{osadchy-04}. 128KBDjVu
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106. Leon Bottou and Yann LeCun: Graph Transformer Networks for Image Recognition, Proceedings of ISI, (invited paper), 2005, \cite{bottou-05}. 50KBDjVu
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105. Yann LeCun, Fu-Jie Huang and Leon Bottou: Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting, Proceedings of CVPR'04, IEEE Press, 2004, \cite{lecun-04}. 308KBDjVu
662KBPDF
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104. Léon Bottou and Yann LeCun: On-line Learning for Very Large Datasets, J. Applied Stochastic Models in Business and Industry, 2004, \cite{bottou-lecun-04a}. 109KBDjVu
158KBPDF
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103. Leon Bottou and Yann LeCun: Large Scale Online Learning, Advances in Neural Information Processing Systems (NIPS 2003), 16, MIT Press, 2004, \cite{bottou-lecun-04b}. 70KBDjVu
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102. L. K. Saul, D. D. Lee, C. L. Isbell and Y. LeCun: Real time voice processing with audiovisual feedback: toward autonomous agents with perfect pitch., Advances in Neural Information Processing Systems (NIPS 2002), 15, MIT Press, 2003, \cite{saul-02}. 110KBDjVu
634KBPDF
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101. Y. LeCun and L. Bottou: Lush Reference Manual, code available at http://lush.sourceforge.net, 2002, \cite{lecun-bottou-02}.

100. Patrick Haffner, Leon Bottou, Yann LeCun and Luc Vincent: A General Segmentation Scheme for DjVu Document Compression, Proceedings of the International Symposium on Mathematical Morphology (ISMM'02), CSIRO publications, Sydney, Australia, April 2002, \cite{haffner-2002}. 195KBDjVu
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99. Advances in Neural Information Processing Systems: Proceedings of the First 12 Conferences, in Jordan, M. I. and LeCun, Y. and Solla, S. A. (Eds), MIT Press, (CDROM), 2001, \cite{jordan-lecun-solla-01}.

98. L. Bottou, P. Haffner and Y. LeCun: Conversion of Digital Documents to Multilayer Raster Formats, Proceedings of the International Conference on Document Analysis and Recognition, september 2001, \cite{bottou-01a}. 119KBDjVu
683KBPDF
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97. Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document Recognition, Intelligent Signal Processing, 306-351, IEEE Press, 2001, \cite{lecun-01a}. 852KBDjVu
955KBPDF
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96. L. Bottou, P. Haffner and Y. LeCun: Efficient Conversion of Digital Documents to Multilayer Raster Formats, International Conference on Document Analysis and Recognition (ICDAR'01), Seattle, September 2001, \cite{bottou-01}. 119KBDjVu
683KBPDF
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95. Y. LeCun, L. Bottou, A. Erofeev, P. Haffner and W. Riemers: DjVu Document Browsing with On-Demand loading and rendering of image components, Internet Imaging, San Jose, January 2001, \cite{lecun-01}. 104KBDjVu
189KBPDF
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94. P. Y. Simard, Y. LeCun, J. S. Denker and B. Victorri: Transformation Invariance in Pattern Recognition -- Tangent Distance and Tangent Propagation, International Journal of Imaging Systems and Technology, 11(3), 2001, \cite{simard-00}. 286KBDjVu
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93. Leon Bottou, P. Haffner, Y. LeCun, Howard P., P. Vincent and B. Riemers: DjVu: Un Systeme de Compression d'Images pour la Distribution Réticulaire de Documents Numérisés. (DjVu: an image compression system for distributing scanned document on the Internet), Conférence Internationale Francophone sur L'Ecrit et le Document, Lyon, France, July 2000, \cite{bottou-00}. 110KBDjVu
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92. P. Haffner, Y. LeCun, Leon Bottou, Howard P., P. Vincent and B. Riemers: Color Documents on the Web with DjVu, International Conference on Image Processing, 1:239-243, Kobe, Japan, October 1999, \cite{haffner-99a}. 81KBDjVu
185KBPDF
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91. Y. LeCun, P. Haffner, L. Bottou and Y. Bengio: Object Recognition with Gradient-Based Learning, in Forsyth, D. (Eds), Feature Grouping, Springer, 1999, \cite{lecun-99}. 355KBDjVu
440KBPDF
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90. P. Haffner, Leon Bottou, Howard P. and Yann LeCun: DjVu: Analyzing and Compressing Scanned Documents for Internet Distribution, International Conference on Document Analysis and Recognition (ICDAR), 625-628, 1999, \cite{haffner-99}. 87KBDjVu
1675KBPDF
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89. P. Simard, L. Bottou, P. Haffner and Y. LeCun: Boxlets: a fast convolution algorithm for neural networks and signal processing, Advances in Neural Information Processing Systems (NIPS 1998), 11, MIT Press, 1999, \cite{simard-99}. 392KBDjVu
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88. R. Chellappa, K. Fukushima, A. Katsaggelos, S.-Y. Kung, Y. LeCun, N. M. Nasrabadi and T. A. Poggio: Applications of Artificial Neural Networks to Image Processing (guest editorial), IEEE Transactions on Image Processing, 7(8):1093-1097, August 1998, \cite{chellappa-98}. 82KBDjVu
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87. Y. LeCun, L. Bottou, P. Haffner and P. Howard: DjVu: a compression method for distributing scanned documents in color over the internet, Color 6, IST, 1998, \cite{lecun-98c}. 119KBDjVu
425KBPDF
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86. Y. LeCun, L. Bottou, G. Orr and K. Muller: Efficient BackProp, in Orr, G. and Muller K. (Eds), Neural Networks: Tricks of the trade, Springer, 1998, \cite{lecun-98b}. 284KBDjVu
511KBPDF
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85. P. Simard, Y. LeCun, J. Denker and B. Victorri: Transformation Invariance in Pattern Recognition, Tangent Distance and Tangent Propagation, in Orr, G. and Muller K. (Eds), Neural Networks: Tricks of the trade, Springer, 1998, \cite{simard-98}. 284KBDjVu
446KBPDF
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84. B. Haskell, P. Howard, Y. LeCun, A. Puri, J. Ostermann, R. Civanlar, L. Rabiner, L. Bottou and P. Haffner: Image and Video Coding - Emerging Standards and Beyond, IEEE Transaction CSVT, 8(7):814-837, November 1998, \cite{haskell-98}. 617KBDjVu
  
  

83. L. Bottou, P. Haffner, P. Howard, P. Simard, Y. Bengio and Y. LeCun: High Quality Document Image Compression with DjVu, Journal of Electronic Imaging, 7(3):410-425, July 1998, \cite{bottou-98}. 449KBDjVu
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82. P. Haffner, L. Bottou, P. Howard, P. Simard, Y. Bengio and Y. LeCun: Browsing through High Quality Document Images with DjVu, Proc. of Advances in Digital Libraries 98, 309-318, IEEE, 1998, \cite{haffner-98}. 263KBDjVu
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81. Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998, \cite{lecun-98}. 842KBDjVu
955KBPDF
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80. R. Cox, B. Haskell, Y. LeCun, B. Shahraray and L. Rabiner: On the application of multimedia processing to telecommunications, Proceedings of the IEEE, 86(5):755-824, May 1998, \cite{cox-98}. 1604KBDjVu
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79. M. Rahim, Y. Bengio and Y. LeCun: Discriminative feature and model design for automatic speech recognition, Proc. of Eurospeech, Rhodes, Greece, 1997, \cite{rahim-bengio-lecun-97}. 65KBDjVu
162KBPDF
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78. R. Cox, B. Haskell, Y. LeCun, B. Shahraray and L. Rabiner: On the application of multimedia processing to telecommunications, Proc. of International Conference on Image Processing, 1:5-8, IEEE, San Francisco, 1997, \cite{cox-97}.

77. L. Bottou, Y. LeCun and Y. Bengio: Global Training of Document Processing Systems using Graph Transformer Networks, Proc. of Computer Vision and Pattern Recognition, 490-494, IEEE, Puerto-Rico, 1997, \cite{bottou-lecun-bengio-97}. 106KBDjVu
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76. Y. LeCun, L. Bottou and Y. Bengio: Reading Checks with graph transformer networks, International Conference on Acoustics, Speech, and Signal Processing, 1:151-154, IEEE, Munich, 1997, \cite{lecun-bottou-bengio-97}. 72KBDjVu
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75. Y. Bengio, Y. LeCun, C. Nohl and C. Burges: LeRec: A NN/HMM Hybrid for On-Line Handwriting Recognition, Neural Computation, 7(6):1289-1303, November 1995, \cite{bengio-95}. 103KBDjVu
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74. Y. LeCun, L. D. Jackel, L. Bottou, A. Brunot, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, U. A. Muller, E. Sackinger, P. Simard and V. Vapnik: Comparison of learning algorithms for handwritten digit recognition, in Fogelman, F. and Gallinari, P. (Eds), International Conference on Artificial Neural Networks, 53-60, EC2 & Cie, Paris, 1995, \cite{lecun-95b}. 69KBDjVu
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73. Y. LeCun, L. D. Jackel, L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, U. A. Muller, E. Sackinger, P. Simard and V. Vapnik: Learning Algorithms For Classification: A Comparison On Handwritten Digit Recognition, in Oh, J. H. and Kwon, C. and Cho, S. (Eds), Neural Networks: The Statistical Mechanics Perspective, 261-276, World Scientific, 1995, \cite{lecun-95a}. 111KBDjVu
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72. Y. LeCun and Y. Bengio: Convolutional Networks for Images, Speech, and Time-Series, in Arbib, M. A. (Eds), The Handbook of Brain Theory and Neural Networks, MIT Press, 1995, \cite{lecun-bengio-95a}. 65KBDjVu
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71. Y. LeCun and Y. Bengio: Pattern Recognition and Neural Networks, in Arbib, M. A. (Eds), The Handbook of Brain Theory and Neural Networks, MIT Press, 1995, \cite{lecun-bengio-95b}. 99KBDjVu
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70. L. Jackel, M. Battista, H. Baird, J. Ben, J. Bromley, C. Burges, E. Cosatto, J. Denker, H. Graf, H. Katseff, Y. LeCun, C. Nohl, E. Sackinger, J. Shamilian, T. Shoemaker, C. Stenard, I. Strom, R. Ting, T. Wood and Zuraw C.: Neural-Net Applications in Character Recognition and Document Analysis, Neural-Net Applications in Telecommunications, Kluwer Academic Publishers, 1995, \cite{jackel-95}. 219KBDjVu
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69. W Chiang, C. Cortes, L. Jackel, Y. LeCun, W. Lee, E. Pednault and Vapnik V.: Predicting TransportPath Degradation/Failure Based on Recent Performance History, Proc of Symposium on Intelligent Systems in Communications and Power (SISCAP '94), IEEE, 1994, \cite{chiang-94a}.

68. N. Matic, D. Henderson, Y. Le Cun and Y. Bengio: Pen-based visitor registration system (PENGUIN), Conference Record of the Twenty-Eighth Asilomar Conference on ignals, Systems and Computers, IEEE, 1994, \cite{matic-94}. 194KBDjVu
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67. H. Drucker, C. Cortes, L. D. Jackel, Y. LeCun and V. Vapnik: Boosting and Other Ensemble Methods, Neural Computation, 6(6), November 1994, \cite{drucker-94a}.

66. L. Bottou, C. Cortes, J.S. Denker, H. Drucker, I. Guyon, L.D. Jackel, Y. LeCun, U.A. Muller, E. Sackinger, P. Simard and V. Vapnik: Comparison of classifier methods: a case study in handwritten digit recognition, in IAPR (Eds), Proc. of the International Conference on Pattern Recognition, II:77-82, IEEE, Jerusalem, October 1994, \cite{bottou-94}. 62KBDjVu
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65. P. Simard, Y. LeCun and J. Denker: Memory Based Character Recognition Using a Transformation Invariant Metric, in IAPR (Eds), Proc. of the International Conference on Pattern Recognition, II:262-267, IEEE, Jerusalem, October 1994, \cite{simard-lecun-denker-94}. 101KBDjVu
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64. Y. LeCun and Y. Bengio: word-level training of a handwritten word recognizer based on convolutional neural networks, in IAPR (Eds), Proc. of the International Conference on Pattern Recognition, II:88-92, IEEE, Jerusalem, October 1994, \cite{lecun-bengio-94}. 71KBDjVu
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63. Y. Bengio and Y. LeCun: word normalization for on-line handwritten word recognition, in IAPR (Eds), Proc. of the International Conference on Pattern Recognition, II:409-413, IEEE, Jerusalem, October 1994, \cite{bengio-lecun-94}. 54KBDjVu
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62. V. Vapnik, E. Levin and Y. LeCun: Measuring the VC-dimension of a Learning Machine, Neural Computation, 6(5):851-876, 1994, \cite{vapnik-levin-lecun-94}. 134KBDjVu
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61. Y. Bengio, Y. LeCun and D. Henderson: Globally Trained Handwritten Word Recognizer using Spatial Representation, Space Displacement Neural Networks and Hidden Markov Models, in Cowan, J. and Tesauro, G. (Eds), Advances in Neural Information Processing Systems (NIPS 1993), 6, Morgan Kaufmann, 1993, \cite{bengio-lecun-henderson-94}. 64KBDjVu
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60. J. Bromley, I. Guyon, Y. LeCun, E. Sackinger and R. Shah: Signature Verification using a Siamese Time Delay Neural Network, in Cowan, J. and Tesauro, G. (Eds), Advances in Neural Information Processing Systems (NIPS 1993), 6, Morgan Kaufmann, 1993, \cite{bromley-94}. 117KBDjVu
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59. H. Drucker, C. Cortes, L. D. Jackel, Y. LeCun and V. Vapnik: Boosting and Other Ensemble Methods, Proc of the 1994 Machine Learning conference, 1994, \cite{drucker-94}.

58. R. Vaillant, C. Monrocq and Y. LeCun: Original approach for the localisation of objects in images, IEE Proc on Vision, Image, and Signal Processing, 141(4):245-250, August 1994, \cite{vaillant-monrocq-lecun-94}. 165KBDjVu
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57. R. Vaillant, C. Monrocq and Y. LeCun: An Original approach for the localisation of objects in images, International Conference on Artificial Neural Networks, 26-30, 1993, \cite{vaillant-monrocq-lecun-93}.

56. C.J. Burges, J. Ben, J.S. Denker and Y. and. Nohl C.R. LeCun: Off Line Recognition of Handwritten Postal Words Using Neural Networks., International Journal of Pattern Recognition and Artificial Intelligence, 7(4):689-704, 1993, \cite{burges-93}.

55. J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. LeCun, C. Moore, E. Sackinger and R. Shah: Signature Verification using a Siamese Time Delay Neural Network, International Journal of Pattern Recognition and Artificial Intelligence, 7(4), August 1993, \cite{bromley-93}. 190KBDjVu
  
  

54. Y. LeCun, Y. Bengio, D. Henderson, A. Weisbuch, H. Weissman and Jackel L.: on-line handwriting recognition with neural networks: spatial representation versus temporal representation., Proc. International Conference on handwriting and drawing., Ecole Nationale Superieure des Telecommunications, 1993, \cite{lecun-93}. 44KBDjVu
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53. Q.Z. Wu, Y. LeCun, L. D. Jackel and B.S. Jeng: on-line recognition of limited vocabulary chinese character using multiple convolutional neural networks, Proc. of the 1993 IEEE International Symposium on circuits and systems, 4:2435-2438, IEEE, 1993, \cite{wu-93}. 69KBDjVu
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52. Yann LeCun and John S. Denker: Natural versus Universal Probability Complexity, and Entropy, IEEE Workshop on the Physics of Computation, 122-127, IEEE, 1992, \cite{denker-lecun-93}. 102KBDjVu
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51. P. Simard, Y. LeCun and Denker J.: efficient pattern recognition using a new transformation distance, in Hanson, S. and Cowan, J. and Giles, L. (Eds), Advances in Neural Information Processing Systems (NIPS 1992), 5, Morgan Kaufmann, 1993, \cite{simard-lecun-denker-93}. 79KBDjVu
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50. Y. LeCun, P. Simard and B. Pearlmutter: Automatic learning rate maximization by on-line estimation of the Hessian's eigenvectors, in Hanson, S. and Cowan, J. and Giles, L. (Eds), Advances in Neural Information Processing Systems (NIPS 1992), 5, Morgan Kaufmann Publishers, San Mateo, CA, 1993, \cite{lecun-simard-pearlmutter-93}. 51KBDjVu
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49. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel: Handwritten digit recognition with a back-propagation network, in Lisboa P.G.J. (Eds), Neural Netwotks, current applications, Chappman and Hall, 1992, \cite{lecun-92a}.

48. P. Simard, Y. LeCun, J. Denker and B. Victorri: An efficient algorithm for learning invariances in adaptive classifiers, Proc. of International Conference on Pattern Recognition, The Hague, 651-655, IAPR, 1992, \cite{simard-92a}.

47. H. Drucker and Y LeCun: Improving Generalization Performance Using Double Backpropagation, IEEE Transaction on Neural Networks, 3(6):991-997, 1992, \cite{drucker-lecun-92}. 177KBDjVu
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46. O. Matan, J. Bromley, C. Burges, J. Denker, L. Jackel, Y. LeCun, E Pednault, W. Satterfield, C Stenard and T. Thompson: Reading Handwritten Digits: A Zip Code Recognition System., IEEE Computer, 25(7):59-63, July 1992, \cite{matan-92}. 333KBDjVu
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45. Y. LeCun: A theoretical framework for Back-Propagation, in Mehra, P. and Wah, B. (Eds), Artificial Neural Networks: concepts and theory, IEEE Computer Society Press, Los Alamitos, CA, 1992, \cite{lecun-92b}. 111KBDjVu
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44. Y. LeCun, L. D. Jackel, B. Boser, J. S. Denker, H. P. Graf, I. Guyon, D. Henderson, R. E. Howard and W. Hubbard: Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic Learning, in Sanchez-Sinencio, E. and Lau, C. (Eds), Artificial Neural Networks, 463-468, IEEE press, 1992, \cite{lecun-92}.

43. Eduard Säckinger, Bernhard Boser, Jane Bromley, Yann LeCun and Lawrence D. Jackel: Application of the ANNA Neural Network Chip to High-Speed Character Recognition, IEEE Transaction on Neural Networks, 3(2):498-505, March 1992, \cite{saeckinger-92}. 208KBDjVu
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42. B. Boser, E. Sackinger, J. Bromley, Y. LeCun and L. Jackel: An analog neural network processor with programmable topology, IEEE Journal of Solid-State Circuits, 26(12):2017-2025, December 1991, \cite{boser-92}. 227KBDjVu
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41. B. Boser, E. Sackinger, J. Bromley, Y. LeCun and L. Jackel: Hardware requirements for neural network pattern classifiers, IEEE Micro, 12(1):32-40, February 1992, \cite{boser-92a}. 320KBDjVu
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40. Ofer Matan, Christopher J. C. Burges, Yann LeCun and John S. Denker: Multi-Digit Recognition Using a Space Displacement Neural Network, in J. M. Moody and S. J. Hanson and R. P. Lippman (Eds), Neural Information Processing Systems (NIPS 1991), 4, Morgan Kaufmann Publishers, San Mateo, CA, 1992, \cite{matan-92a}. 111KBDjVu
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39. C. J. C. Burges, O. Matan, Y LeCun, J. S. Denker, L. D. Jackel, C. E. Stenard, C. R. Nohl and J. I. Ben: Shortest path segmentation: A method for training a neural network to recognize character strings, Proceedings of the 1992 International Joint Conference on Neural Networks, 3:165-172, Baltimore, Maryland, 1992, \cite{burges-92}. 204KBDjVu
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38. P. Simard and Y. LeCun: Reverse TDNN: an architecture for trajectory generation, in J. M. Moody and S. J. Hanson and R. P. Lippman (Eds), Advances in Neural Information Processing Systems (NIPS 1991), 4, Morgan Kaufman, Denver, CO, 1992, \cite{simard-lecun-92}. 94KBDjVu
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37. P. Simard, B. Victorri, Y. LeCun and J. Denker: Tangent Prop: a formalism for specifying selected invariances in adaptive networks, in J. M. Moody and S. J. Hanson and R. P. Lippman (Eds), Advances in Neural Information Processing Systems (NIPS 1991), 4, Morgan Kaufman, Denver, CO, 1992, \cite{simard-92}. 63KBDjVu
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36. I. Guyon, D. Henderson, P. Albrecht, Y. LeCun and J. Denker: writer independentand writer adaptive neural network for on-line character recognition, in Impedovo, S. and Simon, J.C. (Eds), From Pixels to Features III: Frontiers in handwriting recognition, 493-506, Elsevier, 1992, \cite{guyon-92}. 190KBDjVu
  
  

35. B.E. Boser, E. Sackinger, J. Bromley, Y. LeCun, R.E. Howard and L.D. Jackel: An analog neural network processor and its application to high-speed character recognition, Proceedings of the International Joint Conference on Neural Networks, 1:415-420, IEEE Press, Seattle, WA, July 1991, \cite{boser-91}. 156KBDjVu
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34. H. Drucker and Y LeCun: Improving Generalization Performance in Character Recognition, Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, 198-207, IEEE Press, catalog number 91TH0385-5, ISBN 0-7803-0118-8, 1991, \cite{drucker-lecun-91a}. 98KBDjVu
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33. H. Drucker and Y LeCun: Double Backpropagation: Increasing Generalization Performance, Proceedings of the International Joint Conference on Neural Networks, 2:145-150, IEEE Press, Seattle, WA, July 1991, \cite{drucker-lecun-91}. 97KBDjVu
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32. I. Kanter, Y. LeCun and S. Solla: Second-order properties of error surfaces: learning time and generalization, in Lippmann, R. and Moody, J. and Touretzky, D. (Eds), Advances in Neural Information Processing Systems (NIPS 1990), 3, Morgan Kaufman, Denver, CO, April 1991, \cite{kanter-lecun-solla-91}. 71KBDjVu
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31. J. S. Denker and Y. LeCun: transforming neural-net output levels to probability distributions, in Lippmann, R. and Moody, J. and Touretzky, D. (Eds), Advances in Neural Information Processing Systems (NIPS 1990), 3, Morgan Kaufman, Denver, CO, April 1991, \cite{denker-lecun-91}. 89KBDjVu
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30. I. Guyon, P. Albrecht, Y. LeCun, J. S. Denker and Hubbard W.: design of a neural network character recognizer for a touch terminal, Pattern Recognition, 24(2):105-119, 1991, \cite{guyon-91}. 300KBDjVu
  
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29. Y. LeCun, I. Kanter and S. Solla: Eigenvalues of covariance matrices: application to neural-network learning, Physical Review Letters, 66(18):2396-2399, May 1991, \cite{lecun-kanter-solla-91}. 73KBDjVu
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28. S. Solla and Y. LeCun: Constrained Neural Networks for Pattern Recognition, in Antognetti, P. and Milutinovic, V. (Eds), Neural Networks: Concepts, Applications and Implementations Vol IV, 142-161, Prentice Hall, 1991, \cite{solla-lecun-91}. 190KBDjVu
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27. R.E. Howard, B. Boser, J.S. Denker, H.P. Graf, D. Henderson, W. Hubbard, L.D. Jackel, Y. Le Cun and H. S. Baird: Optical character recognition: a technology driver for neural networks, IEEE International Symposium on Circuits and Systems, 1990, 3:2433-2436, 1990, \cite{howard-90}. 160KBDjVu
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26. O. Matan, R. K. Kiang, C. E. Stenard, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel and Y. LeCun: Handwritten character recognition using neural network architectures, Proc. of the 4th US Postal Service Advanced Technology Conference, Washington D.C., November 1990, \cite{matan-90}. 82KBDjVu
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25. I. Guyon, P. Albrecht, Y. LeCun, J. S. Denker and W. Hubbard: A time delay neural network character recognizer for a touch terminal, Proc. of the International Neural Network Conference, Paris, june 1990, \cite{guyon-90}.

24. Y. LeCun, L. D. Jackel, H. P. Graf, B. Boser, J. S. Denker, I. Guyon, D. Henderson, R. E. Howard and S. Hubbard, Solla: Optical Character Recognition and Neural-Net Chips, Proc. of the International Neural Network Conference, Paris, june 1990, \cite{lecun-90f}.

23. Y. LeCun, O. Matan, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel and H. S. Baird: Handwritten Zip Code Recognition with Multilayer Networks, in IAPR (Eds), Proc. of the International Conference on Pattern Recognition, II:35-40, IEEE, Atlantic City, invited paper, 1990, \cite{lecun-90e}. 181KBDjVu
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22. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel and Baird H. S.: Constrained Neural Network for Unconstrained Handwritten Digit Recognition, in Suen, C (Eds), Frontiers in Handwriting Recognition, CENPARMI, Concordia University, Montreal, 1990, \cite{lecun-90d}.

21. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel: Handwritten digit recognition with a back-propagation network, in Touretzky, David (Eds), Advances in Neural Information Processing Systems (NIPS 1989), 2, Morgan Kaufman, Denver, CO, 1990, \cite{lecun-90c}. 77KBDjVu
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20. Yann LeCun, J. S. Denker, S. Solla, R. E. Howard and L. D. Jackel: Optimal Brain Damage, in Touretzky, David (Eds), Advances in Neural Information Processing Systems (NIPS 1989), 2, Morgan Kaufman, Denver, CO, 1990, \cite{lecun-90b}. 54KBDjVu
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19. L.D. Jackel, B. Boser, J.S. Denker, H.P. Graf, Y. Le Cun, I. Guyon, D. Henderson, R.E. Howard, W. Hubbard and S.A. Solla: Hardware requirements for neural-net optical character recognition, Proc. of the International Joint Conference on Neural Networks, 2:855-861, 1990, \cite{jackel-90b}. 175KBDjVu
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18. L. Jackel, B. Boser, H.-P. Graf, J. Denker, Y. LeCun, D. Henderson, O. Matan, R. Howard and H. Baird: VLSI Implementation of Electronic Neural Networks: and Example in Character Recognition, in IEEE (Eds), IEEE International Conference on Systems, Man, and Cybernetics, 320-322, Los Angeles, CA, November 1990, \cite{jackel-90}. 124KBDjVu
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17. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1(4):541-551, Winter 1989, \cite{lecun-89e}. 105KBDjVu
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16. Y. LeCun, L. D. Jackel, B. Boser, J. S. Denker, H. P. Graf, I. Guyon, D. Henderson, R. E. Howard and W. Hubbard: Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic Learning, in Fogelman, F. and Herault, J. and Burnod, Y. (Eds), Neurocomputing, Algorithms, Architectures and Applications, Springer, Les Arcs, France, 1989, \cite{lecun-89d}.

15. Y. LeCun, L. D. Jackel, B. Boser, J. S. Denker, H. P. Graf, I. Guyon, D. Henderson, R. E. Howard and W. Hubbard: Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic Learning, IEEE Communication, 41-46, invited paper, November 1989, \cite{lecun-89c}. 139KBDjVu
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14. Yann LeCun, Conrad C. Galland and Hinton. Geoffrey E.: GEMINI: Gradient estimation through matrix inversion after noise injection, in Touretzky, David (Eds), Advances in Neural Information Processing Systems (NIPS 1988), 1, Morgan Kaufman, Denver, CO, 1989, \cite{lecun-89b}. 97KBDjVu
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13. Y. LeCun: Generalization and Network Design Strategies, in Pfeifer, R. and Schreter, Z. and Fogelman, F. and Steels, L. (Eds), Connectionism in Perspective, Elsevier, Zurich, Switzerland, an extended version was published as a technical report of the University of Toronto, 1989, \cite{lecun-89}. 170KBDjVu
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12. Y. LeCun: A theoretical framework for Back-Propagation, in Touretzky, D. and Hinton, G. and Sejnowski, T. (Eds), Proceedings of the 1988 Connectionist Models Summer School, 21-28, Morgan Kaufmann, CMU, Pittsburgh, Pa, 1988, \cite{lecun-88}. 111KBDjVu
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11. Y. LeCun and Léon-Yves Bottou: SN: A Simulator For Connectionist Models, User Manual, March 1988, \cite{lecun-bottou-88}.

10. S. Becker and Y. LeCun: Improving the Convergence of Back-Propagation Learning with Second-Order Methods, in Touretzky, D. and Hinton, G. and Sejnowski, T. (Eds), Proc. of the 1988 Connectionist Models Summer School, 29-37, Morgan Kaufman, San Mateo, 1989, \cite{becker-lecun-89}. 156KBDjVu
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9. L.-Y. Bottou and Y. LeCun: SN: A Simulator for Connectionist Models, Proceedings of NeuroNimes 88, Nimes, France, 1988, \cite{bottou-lecun-88}.

8. Y. LeCun: Modeles connexionnistes de l'apprentissage (connectionist learning models), June 1987, \cite{lecun-87}. 2541KBDjVu
  
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7. Y. LeCun and F. Fogelman-Soulie: Modeles connexionnistes de l'apprentissage, Intellectica, special issue apprentissage et machine, March 1987, \cite{lecun-87b}. 480KBDjVu
  
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6. F. Fogelman-Soulié, P. Gallinari, Y. LeCun and S. Thiria: Automata networks and artificial intelligence, Automata networks in computer science, theory and applications, 133-186, Princeton University Press, 1987, \cite{fogelman-gallinari-lecun-thiria-87}. 711KBDjVu
  
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5. P. Gallinari, Y. LeCun, S. Thiria and Fogelman-Soulie F.: Mémoires associatives distribuées: une comparaison (distributed associative memories: a comparison), Proceedings of COGNITIVA 87, Cesta-Afcet, Paris, La Villette, May 1987, \cite{gallinari-87}. 164KBDjVu
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4. F. Fogelman-Soulié, P. Gallinari, Y. LeCun and S. Thiria: generalization using back-propagation, Proc. of the first International Conference on Neural Networks, IEEE, San Diego, California, June 1987, \cite{fogelman-gallinari-lecun-thiria-87b}.

3. F. Fogelman-Soulié, P. Gallinari, Y. LeCun and S. Thiria: Learning on automata networks, Proc. of Congrès d'Intelligence Artificielle de Marseille, IIRIAM, Marseille, France, 1987, \cite{fogelman-gallinari-lecun-thiria-87c}.

2. Y. LeCun: Learning Processes in an Asymmetric Threshold Network, in Bienenstock, E. and Fogelman-Soulié, F. and Weisbuch, G. (Eds), Disordered systems and biological organization, 233-240, Springer-Verlag, Les Houches, France, 1986, \cite{lecun-86}. 124KBDjVu
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1. Y. LeCun: Une procédure d'apprentissage pour réseau a seuil asymmetrique (a Learning Scheme for Asymmetric Threshold Networks), Proceedings of Cognitiva 85, 599-604, Paris, France, 1985, \cite{lecun-85}. 128KBDjVu
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