Research output

Research output per year 1985 1992 1994 2007 2010 2012 2013 2014 2015 2022 2023 2024 2025

• 161 Conference contribution
• 55 Article
• 42 Paper
• 32 Conference article
• 38 More
  • 17 Chapter (peer-reviewed)
  • 7 Editorial
  • 5 Review article
  • 3 Chapter
  • 2 Other report
  • 2 Other contribution
  • 1 Comment/debate
  • 1 Digital or Visual Products

Research output per year

Research output per year

Search results

• 2025

  EgoPet: Egomotion and Interaction Data from an Animal’s Perspective

  Bar, A., Bakhtiar, A., Tran, D., Loquercio, A., Rajasegaran, J., LeCun, Y., Globerson, A. & Darrell, T., 2025, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. & Varol, G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 377-394 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15095 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Artificial Intelligence 100%
  • Behavior (Neuroscience) 100%
  • Locomotion 100%
  • multi agent 50%
  • Complex Task 50%
• 2024

  Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

  Tong, S., Liu, Z., Zhai, Y., Ma, Y., Lecun, Y. & Xie, S., 2024, Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024. IEEE Computer Society, p. 9568-9578 11 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Eyes Shut 100%
  • Contrastive Language-image Pre-training 100%
  • Large Language Models 100%
  • Multimodal LLM 100%
  • Visual Patterns 66%
• 2023

  Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need

  Cabannes, V., Bottou, L., Lecun, Y. & Balestriero, R., 2023, Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023. Institute of Electrical and Electronics Engineers Inc., p. 16228-16237 10 p. (Proceedings of the IEEE International Conference on Computer Vision).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Self-Supervised Learning 100%
  • Active Learning 66%
  • Learning Framework 33%
  • Semantic Relationship 33%
  • Semisupervised Learning 16%

• Adapting Grounded Visual Question Answering Models to Low Resource Languages

  Wang, Y., Pfeiffer, J., Carion, N., Lecun, Y. & Kamath, A., 2023, Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023. IEEE Computer Society, p. 2596-2605 10 p. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; vol. 2023-June).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Language Modeling 100%
  • Language Resource 100%
  • Visual Question Answering 100%
  • translation quality 50%
  • Popular Approach 50%

• Compact and Optimal Deep Learning with Recurrent Parameter Generators

  Wang, J., Chen, Y., Yu, S. X., Cheung, B. & Lecun, Y., 2023, Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023. Institute of Electrical and Electronics Engineers Inc., p. 3889-3899 11 p. (Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Degree of Freedom 100%
  • Recurrent 100%
  • Deep Learning 100%
  • Optimal Deep Learning 100%
  • Linear Relationship 16%

• Police: Provably Optimal Linear Constraint Enforcement For Deep Neural Networks

  Balestriero, R. & Lecun, Y., 2023, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc., (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Linear Constraints 100%
  • Deep Neural Network 100%
  • Constraint Enforcement 100%
  • Linear Constraint 100%
  • Neural Network 100%
• 2022

  A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training

  Balestriero, R., Misra, I. & LeCun, Y., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Variance 100%
  • Data Augmentation 100%
  • Gaussian Distribution 50%
  • Edge 50%
  • Sample Covariance Matrix 50%

• Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

  Dou, Z. Y., Kamath, A., Gan, Z., Zhang, P., Wang, J., Li, L., Liu, Z., Liu, C., LeCun, Y., Peng, N., Gao, J. & Wang, L., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Object Detection 100%
  • Image Captioning 100%
  • Visual Question Answering 100%
  • Vision-language Pre-training 100%
  • Tasks 100%

• Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods

  Balestriero, R. & LeCun, Y., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Self-Supervised Learning 100%
  • Embedding Method 100%
  • VICReg 100%
  • Supervised Method 50%
  • Laplace Operator 25%

• Decoupled Contrastive Learning

  Yeh, C. H., Hong, C. Y., Hsu, Y. C., Liu, T. L., Chen, Y. & LeCun, Y., 2022, Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings. Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. & Hassner, T. (eds.). Springer Science and Business Media Deutschland GmbH, p. 668-684 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13686 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Contrastive Learning 100%
  • Supervised Learning 100%
  • Self-Supervised Learning 18%
  • Positive Coupling 18%
  • Extensive Training 9%

• Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors

  Shwartz-Ziv, R., Goldblum, M., Souri, H., Kapoor, S., Zhu, C., LeCun, Y. & Wilson, A. G., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Transfer Learning 100%
  • Deep Learning 100%
  • Bayesian Transfer Learning 100%
  • Learning Transfer 100%
  • Performance Gain 50%

• projUNN: efficient method for training deep networks with unitary matrices

  Kiani, B. T., Balestriero, R., LeCun, Y. & Lloyd, S., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Unitary Matrix 100%
  • Baseline Implementation 100%
  • Neural Network 60%
  • Rank Gradient 40%
  • Orthogonal Matrix 20%

• The Effects of Regularization and Data Augmentation are Class Dependent

  Balestriero, R., Bottou, L. & LeCun, Y., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Regularization 100%
  • Data Augmentation 100%
  • Cross-Validation 100%
  • Neural Network 100%
  • Generalization Performance 50%

• VICRegL: Self-Supervised Learning of Local Visual Features

  Bardes, A., Ponce, J. & LeCun, Y., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • local feature 100%
  • Self-Supervised Learning 100%
  • Visual Feature 100%
  • Global Feature 80%
  • Feature Vector 60%
• 2021

  Barlow Twins: Self-Supervised Learning via Redundancy Reduction

  Zbontar, J., Jing, L., Misra, I., LeCun, Y. & Deny, S., 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. ML Research Press, p. 12310-12320 11 p. (Proceedings of Machine Learning Research; vol. 139).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Objective Function 100%
  • Crosscorrelation 100%
  • Correlation Matrix 100%
  • Identitymatrix 100%
  • Moving Average 100%

• MDETR - Modulated Detection for End-to-End Multi-Modal Understanding

  Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I. & Carion, N., 2021, Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021. Institute of Electrical and Electronics Engineers Inc., p. 1760-1770 11 p. (Proceedings of the IEEE International Conference on Computer Vision).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Multi-modal Understanding 100%
  • Phrase Grounding 50%
  • Few-shot Setting 50%
  • Referring Image Segmentation 50%
  • Referring Expression Comprehension 50%

• Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors

  Yun, Z., Chen, Y., Olshausen, B. A. & LeCun, Y., 2021, Deep Learning Inside Out: 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, DeeLIO 2021 - Proceedings, co-located with the Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL-HLT 2021. Agirre, E., Apidianaki, M. & Vulic, I. (eds.). Association for Computational Linguistics (ACL), p. 1-10 10 p. (Deep Learning Inside Out: 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, DeeLIO 2021 - Proceedings, co-located with the Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL-HLT 2021).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Range Dependency 100%
  • Long-Range Dependency 100%
  • Hierarchical Semantics 40%
  • Conventional Prior 40%
• 2019

  1.1 Deep Learning Hardware: Past, Present, and Future

  Lecun, Y., Mar 6 2019, 2019 IEEE International Solid-State Circuits Conference, ISSCC 2019. Institute of Electrical and Electronics Engineers Inc., p. 12-19 8 p. 8662396. (Digest of Technical Papers - IEEE International Solid-State Circuits Conference; vol. 2019-February).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Deep Learning 100%
  • Deep Learning Hardware 100%
  • Neural Deep Learning 50%
  • Software Development Tool 33%
  • Neural Network 33%

• DesIGN: Design inspiration from generative networks

  Sbai, O., Elhoseiny, M., Bordes, A., LeCun, Y. & Couprie, C., 2019, Computer Vision – ECCV 2018 Workshops, Proceedings. Leal-Taixé, L. & Roth, S. (eds.). Springer Verlag, p. 37-44 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11131 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Design Inspiration 100%
  • Fashion Design 100%
  • Generative Networks 100%
  • Network Architecture 100%
  • Image Synthesis 100%

• Unsupervised image matching and object discovery as optimization

  Vo, H. V., Bach, F., Cho, M., Han, K., Lecun, Y., Perez, P. & Ponce, J., Jun 2019, Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019. IEEE Computer Society, p. 8279-8288 10 p. 8953281. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2019-June).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Annotation 100%
  • Optimization Problem 100%
  • Image Matching 100%
  • Computer Vision 100%
  • Unsupervised Learning 100%
• 2018

  A Closer Look at Spatiotemporal Convolutions for Action Recognition

  Tran, D., Wang, H., Torresani, L., Ray, J., Lecun, Y. & Paluri, M., Dec 14 2018, Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society, p. 6450-6459 10 p. 8578773. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Temporal Convolutional Network 100%
  • 2D CNN 66%
  • Residual Learning 33%
  • Convolutional Block 33%
  • HMDB51 33%

• Adversarially regularized autoencoders

  Zhao, J., Kim, Y., Zhang, K., Rush, A. M. & LeCun, Y., 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 9405-9420 16 p. (35th International Conference on Machine Learning, ICML 2018; vol. 13).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Regularized Auto-encoder 100%
  • Deep Latent Variable Models 100%
  • Autoencoder 100%
  • Adversarial Autoencoder 50%
  • Discrete Sequence 50%

• Comparing Dynamics: Deep Neural Networks versus Glassy Systems

  Baity-Jest, M., Sagun, L., Mario, G., Spiglery, S., Arous, G. B., Cammarota, C., Lecun, Y., Vvyart, M. & Biroli, G. J., 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), p. 526-535 10 p. (35th International Conference on Machine Learning, ICML 2018; vol. 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Dynamic Deep Neural Network 100%
  • Neural Network 100%
  • Loss Landscape 50%
  • Statistical Physics 50%

• Predicting future instance segmentation by forecasting convolutional features

  Luc, P., Couprie, C., LeCun, Y. & Verbeek, J., 2018, Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. Hebert, M., Ferrari, V., Sminchisescu, C. & Weiss, Y. (eds.). Springer Verlag, p. 593-608 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11213 LNCS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Convolutional Features 100%
  • Mask R-CNN 40%
  • Segmentation Architecture 20%
  • Video Forecasting 20%
  • Detection Head 20%
• 2017

  Predicting Deeper into the Future of Semantic Segmentation

  Luc, P., Neverova, N., Couprie, C., Verbeek, J. & Lecun, Y., Dec 22 2017, Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., p. 648-657 10 p. 8237339. (Proceedings of the IEEE International Conference on Computer Vision; vol. 2017-October).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Visual Scene Understanding 100%
  • RGB Frames 100%
  • Cityscapes Dataset 100%
  • Segmentation Map 100%

• Tunable efficient unitary neural networks (EUNN) and their application to RNNs

  Jing, L., Shen, Y., Dubcek, T., Peurifoy, J., Skirlo, S., LeCun, Y., Tegmark, M. & Soljačić, M., 2017, 34th International Conference on Machine Learning, ICML 2017. International Machine Learning Society (IMLS), p. 2753-2761 9 p. (34th International Conference on Machine Learning, ICML 2017; vol. 4).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Neural Network 100%
  • Recurrent Neural Network 100%
  • Artificial Neural Network 50%
  • Long Short-Term Memory Network 33%
  • Unitary Space 33%

• Very deep convolutional networks for text classification

  Conneau, A., Schwenk, H., Cun, Y. L. & Barrault, L., 2017, Long Papers - Continued. Association for Computational Linguistics (ACL), p. 1107-1116 10 p. (15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference; vol. 2).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Text Processing 100%
  • Convolutional Network 100%
  • Text Classification 100%
  • Neural Network 100%
  • Recurrent Neural Network 100%
• 2016

  Binary embeddings with structured hashed projections

  Choromanska, A., Choromanski, K., Bojarski, M., Jebara, T., Kumar, S. & Lecun, Y., 2016, 33rd International Conference on Machine Learning, ICML 2016. Balcan, M. F. & Weinberger, K. Q. (eds.). International Machine Learning Society (IMLS), p. 539-554 16 p. (33rd International Conference on Machine Learning, ICML 2016; vol. 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Neural Network 100%
  • Hashing 100%
  • Random Variable 100%
  • Nonlinear Mapping 100%
  • Random Projection 100%

• Recurrent orthogonal networks and long-memory tasks

  Henaff, M., Szlam, A. & Lecun, Y., 2016, 33rd International Conference on Machine Learning, ICML 2016. Weinberger, K. Q. & Balcan, M. F. (eds.). International Machine Learning Society (IMLS), p. 2978-2986 9 p. (33rd International Conference on Machine Learning, ICML 2016; vol. 5).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Information Type 100%
  • Active Area 100%
  • Architectural Strategies 100%
  • Memory Task 100%
  • Synthetic Data 100%

• Very deep multilingual convolutional neural networks for LVCSR

  Sercu, T., Puhrsch, C., Kingsbury, B. & Lecun, Y., May 18 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 4955-4959 5 p. 7472620. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2016-May).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Large Vocabulary Continuous Speech Recognition (LVCSR) 100%
  • Low-resource Speech Recognition 14%
  • Word Error Rate 14%
  • Very Deep Convolutional Neural Network 14%
  • Very Deep Convolutional Network 14%
• 2015

  Audio source separation with discriminative scattering networks

  Sprechmann, P., Bruna, J. & LeCun, Y., 2015, Latent Variable Analysis and Signal Separation - 12th International Conference, LVA/ICA 2015, Proceedings. Koldovský, Z., Vincent, E., Yeredor, A. & Tichavský, P. (eds.). Springer Verlag, p. 259-267 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9237).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Audio Source Separation 100%
  • Scattering Network 100%
  • Deep Neural Network 50%
  • Audio Application 50%
  • Scattering Operator 33%

• Computing the stereo matching cost with a convolutional neural network

  Žbontar, J. & Le Cun, Y., Oct 14 2015, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society, p. 1592-1599 8 p. 7298767. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 07-12-June-2015).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Stereo Matching Cost 100%
  • Convolutional Neural Network 100%
  • Stereo Matching 100%
  • Patch Matching 50%
  • Patch Use 50%

• Deep learning & convolutional networks

  LeCun, Y., May 23 2015, 2015 IEEE Hot Chips 27 Symposium, HCS 2015. Institute of Electrical and Electronics Engineers Inc., 7477328. (2015 IEEE Hot Chips 27 Symposium, HCS 2015).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Convolutional Network 100%
  • Deep Learning 100%
  • Deep Learning Network 100%
  • Depthwise Separable Convolution 50%
  • Embedded Application 33%

• Efficient object localization using convolutional networks

  Lecun, Y., 2015, Computer vision and pattern recognition (CVPR 2015).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• MoDeep: A deep learning framework using motion features for human pose estimation

  Jain, A., Tompson, J., LeCun, Y. & Bregler, C., 2015, Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers. Yang, M.-H., Saito, H., Cremers, D. & Reid, I. (eds.). Springer Verlag, p. 302-315 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9004).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Deep Learning 100%
  • Learning Framework 100%
  • Pose Estimation 100%
  • Motion Feature 100%
  • Deep Learning Framework 100%

• Source separation with scattering Non-Negative Matrix Factorization

  Bruna, J., Sprechmann, P. & Lecun, Y., Aug 4 2015, 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 1876-1880 5 p. 7178296. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; vol. 2015-August).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Matrix (Mathematics) 100%
  • Factorization 100%
  • Analysis Operator 66%
  • Matrix Decomposition 33%
  • Source Separation Methods 33%

• The loss of surface of multilayer networks

  Lecun, Y., 2015, AI and Statistics (AIStats 2015).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• The Loss Surfaces of Multilayer Networks

  Choromanska, A., Henaff, M. B., Mathieu, M., Ben Arous, G. & LeCun, Y., 2015, International Conference on Articial Intelligence and Statistics (AISTATS).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Unsupervised learning of spatiotemporally coherent metrics

  Goroshin, R., Bruna, J., Tompson, J., Eigen, D. & Lecun, Y., Feb 17 2015, 2015 International Conference on Computer Vision, ICCV 2015. Institute of Electrical and Electronics Engineers Inc., p. 4086-4093 8 p. 7410822. (Proceedings of the IEEE International Conference on Computer Vision; vol. 2015 International Conference on Computer Vision, ICCV 2015).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Unsupervised Learning 100%
  • Representation Learning 100%
  • Convolutional Network 66%
  • Feature Metric 50%
  • Unsupervised Feature Learning 50%
• 2014

  Exploiting linear structure within convolutional networks for efficient evaluation

  Denton, E., Zaremba, W., Bruna, J., LeCun, Y. & Fergus, R., 2014, Advances in Neural Information Processing Systems. January ed. Neural information processing systems foundation, Vol. 2. p. 1269-1277 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Large State 100%
  • Convolutional Layer 100%
  • Floating-point Operations 100%
  • Internet Scale 100%
  • Floating-Point Operation 100%

• Signal recovery from pooling representations

  Bruna, J., Szlam, A. & Lecun, Y., 2014, 31st International Conference on Machine Learning, ICML 2014. International Machine Learning Society (IMLS), p. 1585-1598 14 p. (31st International Conference on Machine Learning, ICML 2014; vol. 2).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Pooling Operator 100%
  • Layer Network 100%
  • Image Patch 100%
  • Pooling Layer 100%
  • Rectification 100%

• Spectral networks and locally connected networks on graphs

  Bruna, J., Zaremba, W., Szlam, A. & Lecun, Y., 2014, International Conference on Learning Representations (ICLR2014), CBLS, April 2014. http://openreview.net/document/d332e77d- 459a-4af8-b3ed-55ba9662182c, http://arxiv.org/abs/1312.6203

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
• 2013

  Causal graph-based video segmentation

  Couprie, C., Farabet, C., Lecun, Y. & Najman, L., 2013, 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings. IEEE Computer Society, p. 4249-4253 5 p. 6738875. (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Minimum Spanning Tree 100%
  • Graph-based 100%
  • Causal Graph 100%
  • Spanning Tree 100%
  • Real Time 100%

• Pushing stochastic gradient towards second-order methods - Backpropagation learning with transformations in nonlinearities

  Vatanen, T., Raiko, T., Valpola, H. & LeCun, Y., 2013, Neural Information Processing - 20th International Conference, ICONIP 2013, Proceedings. PART 1 ed. p. 442-449 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8226 LNCS, no. PART 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Nonlinearity 100%
  • Backpropagation Learning 100%
  • Second-order Methods 100%
  • Stochastic Gradient 100%
  • Hidden Neurons 100%

• Real-time adaptive o-road vehicle navigation and terrain classification

  Muller, U. A., Jackel, L. D., Lecun, Y. & Flepp, B., 2013, Unmanned Systems Technology XV. 87410A. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 8741).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • DARPA 100%
  • Road Vehicles 100%
  • Vehicle Navigation 100%
  • Terrain Classification 100%
  • Road Vehicle 100%

• Regularization of neural networks using DropConnect

  Wan, L., Zeiler, M., Zhang, S., LeCun, Y. & Fergus, R., 2013, 30th International Conference on Machine Learning, ICML 2013. PART 3 ed. International Machine Learning Society (IMLS), p. 2095-2103 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Regularization 100%
  • Neural Network 100%
  • DropConnect 100%
  • Generalization Performance 50%
  • Selected Subset 33%
• 2012

  Convolutional neural networks applied to house numbers digit classification

  Sermanet, P., Chintala, S. & Lecun, Y., 2012, ICPR 2012 - 21st International Conference on Pattern Recognition. p. 3288-3291 4 p. 6460867. (Proceedings - International Conference on Pattern Recognition).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Digit Recognition 100%
  • Convolutional Neural Network 100%
  • Multi-stage Features 50%
  • Hierarchical Feature Learning 25%
  • Learning Neural Network 25%

• Deep learning made easier by linear transformations in perceptrons

  Raiko, T., Valpola, H. & Lecun, Y., 2012, Conference on AI and Statistics (JMLR W&CP). Vol. 22. p. 924-932 http://jmlr.csail.mit.edu/proceedings/papers/v22/raiko12/raiko12.pdf

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Fast approximations to structured sparse coding and applications to object classification

  Szlam, A., Gregor, K. & LeCun, Y., 2012, Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings. PART 5 ed. p. 200-213 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 7576 LNCS, no. PART 5).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Approximation (Algorithm) 100%
  • Dictionary Element 100%
  • Fast Approximation 100%
  • Structured Sparse Coding 100%
  • Pseudoinverse 100%

• Learning invariant feature hierarchies

  LeCun, Y., 2012, Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings. PART 1 ed. Springer Verlag, p. 496-505 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 7583 LNCS, no. PART 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Learning Algorithm 100%
  • Computer Vision 100%
  • Unsupervised Learning 100%
  • Parsing 100%
  • Object Category 100%