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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. |
Download BibTeX file
| 187. | Camille Couprie, Clement Farabet, Laurent Najman and Yann LeCun: Toward Real-time Indoor Semantic Segmentation Using Depth Information, JMLR, to appear. Video, 2014, \cite{couprie-jmlr-14}. | 1161KB | DjVu |
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| 186. | Jonatan Tompson, Murphy Stein, Yann LeCun and Ken Perlin: Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks , ACM Transaction on Graphics, to appear. Video, 2014, \cite{tompson-siggraph-14}. | 837KB | DjVu |
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| 185. | Joan Bruna, Arthur Szlam and Yann LeCun: Signal Recovery from Lp Pooling Representations, International Conference on Machine Learning (ICML'14), 2014, \cite{bruna-icml-14}. | 279KB | DjVu |
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| 184. | Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and Yann LeCun: OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, International Conference on Learning Representations (ICLR2014), CBLS, (OpenReview), (Arxiv:1312.6229); Video of the talk, April 2014, \cite{sermanet-iclr-14}. | 451KB | DjVu |
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| 183. | Joan Bruna, Wojciech Zaremba, Arthur Szlam and Yann LeCun: Spectral Networks and Locally Connected Networks on Graphs, International Conference on Learning Representations (ICLR2014), CBLS, (OpenReview), (arXiv:1312.6203), April 2014, \cite{bruna-iclr-14}. | 421KB | DjVu |
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| 182. | Michael Mathieu, Mikael Henaff and Yann LeCun: Fast Training of Convolutional Networks through FFTs, International Conference on Learning Representations (ICLR2014), CBLS, (OpenReview), (arXiv:1312.5851), April 2014, \cite{mathieu-iclr-14}. | 102KB | DjVu |
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| 181. | David Eigen, Jason Rolfe, Rob Fergus and Yann LeCun: Understanding Deep Architectures using a Recursive Convolutional Network, International Conference on Learning Representations (ICLR2014), CBLS, (OpenReview), (arXiv:1312.1847), April 2014, \cite{eigen-iclr-14}. | 136KB | DjVu |
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| 180. | EJ Humphrey, JP Bello and Y LeCun: Feature learning and deep architectures: new directions for music informatics, Journal of Intelligent Information Systems, 41(3):461-481, 2013, \cite{humphrey-13}. | 1190KB | DjVu |
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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}. | 509KB | DjVu |
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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, Video part 1; Video part 2, June 2013, \cite{sermanet-cvpr-13}. | 208KB | DjVu |
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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}. | 738KB | DjVu |
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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}. | 396KB | DjVu |
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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}. | 368KB | DjVu |
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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}. | 229KB | DjVu |
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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}. | 393KB | DjVu |
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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, August 2013, \cite{farabet-pami-13}. | 1144KB | DjVu |
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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}. | 368KB | DjVu |
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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}. | 234KB | DjVu |
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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}. | 284KB | DjVu |
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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}. | 302KB | DjVu |
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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}. | 736KB | DjVu |
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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}. | 595KB | DjVu |
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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}. | 357KB | DjVu |
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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}. | 514KB | DjVu |
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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}. | 1009KB | DjVu |
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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}. | 939KB | DjVu |
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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}. | 274KB | DjVu |
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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}. | 321KB | DjVu |
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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}. | 147KB | DjVu |
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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}. | 130KB | DjVu |
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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}. | 235KB | DjVu |
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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}. | 363KB | DjVu |
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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}. | 239KB | DjVu |
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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}. | 897KB | DjVu |
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