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ORCIDYann LeCun
Yann André LeCun
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- affiliation: New York University, Courant Institute of Mathematical Sciences, USA
- affiliation: Facebook
- award (2018): Turing Award
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2020 – today
- 2023
[c162]Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, Yann LeCun:
Compact and Optimal Deep Learning with Recurrent Parameter Generators. WACV 2023: 3889-3899- [i112]Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, Nicolas Ballas:
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture. CoRR abs/2301.08243 (2023) - [i111]Shoaib Ahmed Siddiqui, David Krueger, Yann LeCun, Stéphane Deny:
Blockwise Self-Supervised Learning at Scale. CoRR abs/2302.01647 (2023) - [i110]Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti:
The SSL Interplay: Augmentations, Inductive Bias, and Generalization. CoRR abs/2302.02774 (2023) - [i109]Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom:
Augmented Language Models: a Survey. CoRR abs/2302.07842 (2023) - [i108]Quentin Garrido, Laurent Najman, Yann LeCun:
Self-supervised learning of Split Invariant Equivariant representations. CoRR abs/2302.10283 (2023) - [i107]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR abs/2303.00633 (2023) - [i106]Vivien Cabannes, Léon Bottou, Yann LeCun, Randall Balestriero:
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need. CoRR abs/2303.15256 (2023) - [i105]Shengbang Tong, Yubei Chen, Yi Ma, Yann LeCun:
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch. CoRR abs/2304.03977 (2023) - [i104]Ravid Shwartz-Ziv, Yann LeCun:
To Compress or Not to Compress - Self-Supervised Learning and Information Theory: A Review. CoRR abs/2304.09355 (2023) - [i103]Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum:
A Cookbook of Self-Supervised Learning. CoRR abs/2304.12210 (2023) - 2022
- [j40]Yutaka Matsuo
, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto:
Deep learning, reinforcement learning, and world models. Neural Networks 152: 267-275 (2022) - [j39]Katrina Evtimova, Yann LeCun:
Sparse Coding with Multi-layer Decoders using Variance Regularization. Trans. Mach. Learn. Res. 2022 (2022) - [c161]Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, Yann LeCun:
Decoupled Contrastive Learning. ECCV (26) 2022: 668-684 - [c160]Adrien Bardes, Jean Ponce, Yann LeCun:
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. ICLR 2022 - [c159]Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian:
Understanding Dimensional Collapse in Contrastive Self-supervised Learning. ICLR 2022 - [c158]Randall Balestriero, Léon Bottou, Yann LeCun:
The Effects of Regularization and Data Augmentation are Class Dependent. NeurIPS 2022 - [c157]Randall Balestriero, Yann LeCun:
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. NeurIPS 2022 - [c156]Randall Balestriero, Ishan Misra, Yann LeCun:
A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training. NeurIPS 2022 - [c155]Adrien Bardes, Jean Ponce, Yann LeCun:
VICRegL: Self-Supervised Learning of Local Visual Features. NeurIPS 2022 - [c154]Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, Jianfeng Gao, Lijuan Wang:
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone. NeurIPS 2022 - [c153]Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Seth Lloyd:
projUNN: efficient method for training deep networks with unitary matrices. NeurIPS 2022 - [c152]Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew G. Wilson:
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS 2022 - [i102]Zengyi Li, Yubei Chen, Yann LeCun, Friedrich T. Sommer:
Neural Manifold Clustering and Embedding. CoRR abs/2201.10000 (2022) - [i101]Randall Balestriero, Ishan Misra, Yann LeCun:
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments. CoRR abs/2202.08325 (2022) - [i100]Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Seth Lloyd:
projUNN: efficient method for training deep networks with unitary matrices. CoRR abs/2203.05483 (2022) - [i99]Randall Balestriero, Léon Bottou, Yann LeCun:
The Effects of Regularization and Data Augmentation are Class Dependent. CoRR abs/2204.03632 (2022) - [i98]Vlad Sobal, Alfredo Canziani, Nicolas Carion, Kyunghyun Cho, Yann LeCun:
Separating the World and Ego Models for Self-Driving. CoRR abs/2204.07184 (2022) - [i97]Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson:
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR abs/2205.10279 (2022) - [i96]Randall Balestriero, Yann LeCun:
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. CoRR abs/2205.11508 (2022) - [i95]Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, Yann LeCun:
On the duality between contrastive and non-contrastive self-supervised learning. CoRR abs/2206.02574 (2022) - [i94]Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, Jianfeng Gao, Lijuan Wang:
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone. CoRR abs/2206.07643 (2022) - [i93]Li Jing, Jiachen Zhu, Yann LeCun:
Masked Siamese ConvNets. CoRR abs/2206.07700 (2022) - [i92]Yubei Chen, Adrien Bardes, Zengyi Li, Yann LeCun:
Intra-Instance VICReg: Bag of Self-Supervised Image Patch Embedding. CoRR abs/2206.08954 (2022) - [i91]Jiachen Zhu, Rafael M. Moraes, Serkan Karakulak, Vlad Sobol, Alfredo Canziani, Yann LeCun:
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning. CoRR abs/2206.10698 (2022) - [i90]Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun:
What Do We Maximize in Self-Supervised Learning? CoRR abs/2207.10081 (2022) - [i89]Wancong Zhang, Anthony GX-Chen, Vlad Sobal, Yann LeCun, Nicolas Carion:
Light-weight probing of unsupervised representations for Reinforcement Learning. CoRR abs/2208.12345 (2022) - [i88]Bobak Toussi Kiani, Randall Balestriero, Yubei Chen, Seth Lloyd, Yann LeCun:
Joint Embedding Self-Supervised Learning in the Kernel Regime. CoRR abs/2209.14884 (2022) - [i87]Grégoire Mialon, Randall Balestriero, Yann LeCun:
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations. CoRR abs/2209.14905 (2022) - [i86]Yubei Chen, Zeyu Yun, Yi Ma, Bruno A. Olshausen, Yann LeCun:
Minimalistic Unsupervised Learning with the Sparse Manifold Transform. CoRR abs/2209.15261 (2022) - [i85]Adrien Bardes, Jean Ponce, Yann LeCun:
VICRegL: Self-Supervised Learning of Local Visual Features. CoRR abs/2210.01571 (2022) - [i84]Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun:
RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank. CoRR abs/2210.02885 (2022) - [i83]Shraman Pramanick, Li Jing, Sayan Nag, Jiachen Zhu, Hardik Shah, Yann LeCun, Rama Chellappa:
VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment. CoRR abs/2210.04135 (2022) - [i82]Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy P. Lillicrap, Adam H. Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Tsao:
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR abs/2210.08340 (2022) - [i81]Shengbang Tong, Xili Dai, Yubei Chen, Mingyang Li, Zengyi Li, Brent Yi, Yann LeCun, Yi Ma:
Unsupervised Learning of Structured Representations via Closed-Loop Transcription. CoRR abs/2210.16782 (2022) - [i80]Randall Balestriero, Yann LeCun:
POLICE: Provably Optimal Linear Constraint Enforcement for Deep Neural Networks. CoRR abs/2211.01340 (2022) - [i79]Vlad Sobal, Jyothir S. V, Siddhartha Jalagam, Nicolas Carion, Kyunghyun Cho, Yann LeCun:
Joint Embedding Predictive Architectures Focus on Slow Features. CoRR abs/2211.10831 (2022) - [i78]Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, Xavier Bresson:
A Generalization of ViT/MLP-Mixer to Graphs. CoRR abs/2212.13350 (2022) - 2021
- [j38]Yoshua Bengio, Yann LeCun, Geoffrey E. Hinton:
Deep learning for AI. Commun. ACM 64(7): 58-65 (2021) - [j37]Baptiste Rozière, Morgane Rivière, Olivier Teytaud, Jérémy Rapin, Yann LeCun, Camille Couprie:
Inspirational Adversarial Image Generation. IEEE Trans. Image Process. 30: 4036-4045 (2021) - [c151]Zeyu Yun, Yubei Chen, Bruno A. Olshausen, Yann LeCun:
Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors. DeeLIO@NAACL-HLT 2021: 1-10 - [c150]Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, Nicolas Carion:
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding. ICCV 2021: 1760-1770 - [c149]Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny:
Barlow Twins: Self-Supervised Learning via Redundancy Reduction. ICML 2021: 12310-12320 - [d1]Xiang Zhang, Junbo Zhao, Yann LeCun:
DBPedia. IEEE DataPort, 2021
- [i77]Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny:
Barlow Twins: Self-Supervised Learning via Redundancy Reduction. CoRR abs/2103.03230 (2021) - [i76]Zeyu Yun, Yubei Chen, Bruno A. Olshausen, Yann LeCun:
Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors. CoRR abs/2103.15949 (2021) - [i75]Aishwarya Kamath, Mannat Singh, Yann LeCun, Ishan Misra, Gabriel Synnaeve, Nicolas Carion:
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding. CoRR abs/2104.12763 (2021) - [i74]Adrien Bardes, Jean Ponce, Yann LeCun:
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. CoRR abs/2105.04906 (2021) - [i73]Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, Yann LeCun:
Recurrent Parameter Generators. CoRR abs/2107.07110 (2021) - [i72]Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, Yann LeCun:
Decoupled Contrastive Learning. CoRR abs/2110.06848 (2021) - [i71]Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian:
Understanding Dimensional Collapse in Contrastive Self-supervised Learning. CoRR abs/2110.09348 (2021) - [i70]Randall Balestriero, Jerome Pesenti, Yann LeCun:
Learning in High Dimension Always Amounts to Extrapolation. CoRR abs/2110.09485 (2021) - [i69]Katrina Evtimova, Yann LeCun:
Sparse Coding with Multi-Layer Decoders using Variance Regularization. CoRR abs/2112.09214 (2021) - 2020
- [c148]Li Jing, Jure Zbontar, Yann LeCun:
Implicit Rank-Minimizing Autoencoder. NeurIPS 2020 - [i68]Li Jing, Jure Zbontar, Yann LeCun:
Implicit Rank-Minimizing Autoencoder. CoRR abs/2010.00679 (2020)
2010 – 2019
- 2019
- [c147]Huy V. Vo, Francis R. Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Pérez, Jean Ponce:
Unsupervised Image Matching and Object Discovery as Optimization. CVPR 2019: 8287-8296 - [c146]Mikael Henaff, Alfredo Canziani, Yann LeCun:
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic. ICLR (Poster) 2019 - [c145]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
The role of over-parametrization in generalization of neural networks. ICLR (Poster) 2019 - [c144]Yann LeCun:
Deep Learning Hardware: Past, Present, and Future. ISSCC 2019: 12-19 - [i67]Mikael Henaff, Alfredo Canziani, Yann LeCun:
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic. CoRR abs/1901.02705 (2019) - [i66]Mohamed Ishmael Belghazi, Maxime Oquab, Yann LeCun, David Lopez-Paz:
Learning about an exponential amount of conditional distributions. CoRR abs/1902.08401 (2019) - [i65]Huy V. Vo, Francis R. Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Pérez, Jean Ponce:
Unsupervised Image Matching and Object Discovery as Optimization. CoRR abs/1904.03148 (2019) - [i64]Morgane Rivière, Olivier Teytaud, Jérémy Rapin, Yann LeCun, Camille Couprie:
Inspirational Adversarial Image Generation. CoRR abs/1906.11661 (2019) - 2018
- [c143]Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, Manohar Paluri:
A Closer Look at Spatiotemporal Convolutions for Action Recognition. CVPR 2018: 6450-6459 - [c142]Othman Sbai, Mohamed Elhoseiny, Antoine Bordes, Yann LeCun, Camille Couprie:
DesIGN: Design Inspiration from Generative Networks. ECCV Workshops (3) 2018: 37-44 - [c141]Pauline Luc, Camille Couprie, Yann LeCun, Jakob Verbeek:
Predicting Future Instance Segmentation by Forecasting Convolutional Features. ECCV (9) 2018: 593-608 - [c140]Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli:
Comparing Dynamics: Deep Neural Networks versus Glassy Systems. ICML 2018: 324-333 - [c139]Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun:
Adversarially Regularized Autoencoders. ICML 2018: 5897-5906 - [c138]Zhilin Yang, Junbo Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun:
GLoMo: Unsupervised Learning of Transferable Relational Graphs. NeurIPS 2018: 8964-8975 - [i63]Xiang Zhang, Yann LeCun:
Byte-Level Recursive Convolutional Auto-Encoder for Text. CoRR abs/1802.01817 (2018) - [i62]Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli:
Comparing Dynamics: Deep Neural Networks versus Glassy Systems. CoRR abs/1803.06969 (2018) - [i61]Pauline Luc, Camille Couprie, Yann LeCun, Jakob Verbeek:
Predicting Future Instance Segmentations by Forecasting Convolutional Features. CoRR abs/1803.11496 (2018) - [i60]Othman Sbai, Mohamed Elhoseiny, Antoine Bordes, Yann LeCun, Camille Couprie:
DeSIGN: Design Inspiration from Generative Networks. CoRR abs/1804.00921 (2018) - [i59]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. CoRR abs/1805.12076 (2018) - [i58]Aditya Ramesh, Yann LeCun:
Backpropagation for Implicit Spectral Densities. CoRR abs/1806.00499 (2018) - [i57]Zhilin Yang, Junbo Jake Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun:
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations. CoRR abs/1806.05662 (2018) - [i56]Xiang Zhang, Yann LeCun:
Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation. CoRR abs/1811.04201 (2018) - [i55]Aditya Ramesh, Youngduck Choi, Yann LeCun:
A Spectral Regularizer for Unsupervised Disentanglement. CoRR abs/1812.01161 (2018) - 2017
- [j36]Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst:
Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Process. Mag. 34(4): 18-42 (2017) - [c137]Xiang Zhang, Yann LeCun:
Universum Prescription: Regularization Using Unlabeled Data. AAAI 2017: 2907-2913 - [c136]Alexis Conneau, Holger Schwenk, Loïc Barrault, Yann LeCun:
Very Deep Convolutional Networks for Text Classification. EACL (1) 2017: 1107-1116 - [c135]Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, Yann LeCun:
Predicting Deeper into the Future of Semantic Segmentation. ICCV 2017: 648-657 - [c134]Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina:
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. ICLR (Poster) 2017 - [c133]Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun:
Tracking the World State with Recurrent Entity Networks. ICLR (Poster) 2017 - [c132]Junbo Jake Zhao, Michaël Mathieu, Yann LeCun:
Energy-based Generative Adversarial Networks. ICLR (Poster) 2017 - [c131]Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott A. Skirlo, Yann LeCun, Max Tegmark, Marin Soljacic:
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs. ICML 2017: 1733-1741 - [i54]Natalia Neverova, Pauline Luc, Camille Couprie, Jakob Verbeek, Yann LeCun:
Predicting Deeper into the Future of Semantic Segmentation. CoRR abs/1703.07684 (2017) - [i53]Mikael Henaff, William F. Whitney, Yann LeCun:
Model-Based Planning in Discrete Action Spaces. CoRR abs/1705.07177 (2017) - [i52]Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun:
Adversarially Regularized Autoencoders for Generating Discrete Structures. CoRR abs/1706.04223 (2017) - [i51]Xiang Zhang, Yann LeCun:
Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean? CoRR abs/1708.02657 (2017) - [i50]Cinna Wu, Mark Tygert, Yann LeCun:
Hierarchical loss for classification. CoRR abs/1709.01062 (2017) - [i49]Mikael Henaff, Junbo Jake Zhao, Yann LeCun:
Prediction Under Uncertainty with Error-Encoding Networks. CoRR abs/1711.04994 (2017) - [i48]Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, Manohar Paluri:
A Closer Look at Spatiotemporal Convolutions for Action Recognition. CoRR abs/1711.11248 (2017) - 2016
- [j35]Jure Zbontar, Yann LeCun:
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches. J. Mach. Learn. Res. 17: 65:1-65:32 (2016) - [j34]Mark Tygert, Joan Bruna, Soumith Chintala, Yann LeCun, Serkan Piantino, Arthur Szlam:
A Mathematical Motivation for Complex-Valued Convolutional Networks. Neural Comput. 28(5): 815-825 (2016) - [c130]Tom Sercu, Christian Puhrsch, Brian Kingsbury, Yann LeCun:
Very deep multilingual convolutional neural networks for LVCSR. ICASSP 2016: 4955-4959 - [c129]Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun:
Binary embeddings with structured hashed projections. ICML 2016: 344-353 - [c128]Mikael Henaff, Arthur Szlam, Yann LeCun:
Recurrent Orthogonal Networks and Long-Memory Tasks. ICML 2016: 2034-2042 - [c127]Michaël Mathieu, Junbo Jake Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun:
Disentangling factors of variation in deep representation using adversarial training. NIPS 2016: 5041-5049 - [c126]Joan Bruna, Pablo Sprechmann, Yann LeCun:
Super-Resolution with Deep Convolutional Sufficient Statistics. ICLR (Poster) 2016 - [c125]Michaël Mathieu, Camille Couprie, Yann LeCun:
Deep multi-scale video prediction beyond mean square error. ICLR (Poster) 2016 - [e7]Yoshua Bengio, Yann LeCun:
4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. 2016 [contents] - [i47]Mikael Henaff, Arthur Szlam, Yann LeCun:
Orthogonal RNNs and Long-Memory Tasks. CoRR abs/1602.06662 (2016) - [i46]Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs:
Phase 1: DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - HPC System Implementation. CoRR abs/1605.00971 (2016) - [i45]Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs:
Phase 2: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Machine Learning Detection Algorithms. CoRR abs/1605.00972 (2016) - [i44]Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs:
Phase 4: DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals - Distributed Processing and Big Data Applications. CoRR abs/1605.00982 (2016) - [i43]Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs:
Phase 3: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Bioacoustic Applicaitons. CoRR abs/1605.00983 (2016) - [i42]Kevin Jarrett, Koray Kavukcuoglu, Karol Gregor, Yann LeCun:
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures? CoRR abs/1606.01535 (2016) - [i41]Alexis Conneau, Holger Schwenk, Loïc Barrault, Yann LeCun:
Very Deep Convolutional Networks for Natural Language Processing. CoRR abs/1606.01781 (2016) - [i40]Artem Provodin, Liila Torabi, Beat Flepp, Yann LeCun, Michael Sergio, Lawrence D. Jackel, Urs Muller, Jure Zbontar:
Fast Incremental Learning for Off-Road Robot Navigation. CoRR abs/1606.08057 (2016) - [i39]Junbo Jake Zhao, Michaël Mathieu, Yann LeCun:
Energy-based Generative Adversarial Network. CoRR abs/1609.03126 (2016) - [i38]Yacine Jernite, Anna Choromanska, David A. Sontag, Yann LeCun:
Simultaneous Learning of Trees and Representations for Extreme Classification, with Application to Language Modeling. CoRR abs/1610.04658 (2016) - [i37]Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina:
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. CoRR abs/1611.01838 (2016) - [i36]