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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 3A. Bietti, J. Mairal.

    Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations, in: Journal of Machine Learning Research, 2019, vol. 20, no 1, pp. 1-49, https://arxiv.org/abs/1706.03078.

    https://hal.inria.fr/hal-01536004
  • 4D. Chen, L. Jacob, J. Mairal.

    Biological Sequence Modeling with Convolutional Kernel Networks, in: Bioinformatics, September 2019, vol. 35, no 18, pp. 3294–3302. [ DOI : 10.1093/bioinformatics/btz094 ]

    https://hal.inria.fr/hal-01632912
  • 5D. Derkach, A. Ruiz, F. M. Sukno.

    Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation, in: International Journal of Computer Vision, October 2019, vol. 127, no 10, pp. 1565-1585. [ DOI : 10.1007/s11263-019-01208-x ]

    https://hal.archives-ouvertes.fr/hal-02267568
  • 6G. Durif, L. Modolo, J. E. Mold, S. Lambert-Lacroix, F. Picard.

    Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis, in: Bioinformatics, October 2019, vol. 20, pp. 4011–4019, https://arxiv.org/abs/1710.11028. [ DOI : 10.1093/bioinformatics/btz177 ]

    https://hal.archives-ouvertes.fr/hal-01649275
  • 7N. Dvornik, J. Mairal, C. Schmid.

    On the Importance of Visual Context for Data Augmentation in Scene Understanding, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, December 2019, pp. 1-15, forthcoming. [ DOI : 10.1109/TPAMI.2019.2961896 ]

    https://hal.archives-ouvertes.fr/hal-01869784
  • 8H. Lin, J. Mairal, Z. Harchaoui.

    An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration, in: SIAM Journal on Optimization, May 2019, vol. 29, no 2, pp. 1408-1443, https://arxiv.org/abs/1610.00960. [ DOI : 10.1137/17M1125157 ]

    https://hal.inria.fr/hal-01376079
  • 9G. Rogez, P. Weinzaepfel, C. Schmid.

    LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, pp. 1-15, forthcoming. [ DOI : 10.1109/TPAMI.2019.2892985 ]

    https://hal.archives-ouvertes.fr/hal-01961189
  • 10P. Tokmakov, C. Schmid, K. Alahari.

    Learning to Segment Moving Objects, in: International Journal of Computer Vision, March 2019, vol. 127, no 3, pp. 282–301, https://arxiv.org/abs/1712.01127. [ DOI : 10.1007/s11263-018-1122-2 ]

    https://hal.archives-ouvertes.fr/hal-01653720

International Conferences with Proceedings

  • 11A. Bietti, J. Mairal.

    On the Inductive Bias of Neural Tangent Kernels, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-24, https://arxiv.org/abs/1905.12173.

    https://hal.inria.fr/hal-02144221
  • 12A. Bietti, G. Mialon, D. Chen, J. Mairal.

    A Kernel Perspective for Regularizing Deep Neural Networks, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, Proceedings of Machine Learning Research, June 2019, vol. 97, pp. 664-674, https://arxiv.org/abs/1810.00363.

    https://hal.inria.fr/hal-01884632
  • 13M. Caron, P. Bojanowski, J. Mairal, A. Joulin.

    Unsupervised Pre-Training of Image Features on Non-Curated Data, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, Proceedings of the International Conference on Computer Vision (ICCV), October 2019, pp. 1-10.

    https://hal.archives-ouvertes.fr/hal-02119564
  • 14D. Chen, L. Jacob, J. Mairal.

    Biological Sequence Modeling with Convolutional Kernel Networks, in: RECOMB 2019 - 23rd Annual International Conference Research in Computational Molecular Biology, Washington DC, United States, Springer, May 2019, pp. 1-2. [ DOI : 10.1007/978-3-030-17083-7 ]

    https://hal.archives-ouvertes.fr/hal-02388776
  • 15D. Chen, L. Jacob, J. Mairal.

    Recurrent Kernel Networks, in: NeurIPS 2019 - Thirty-third Conference Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-19, https://arxiv.org/abs/1906.03200.

    https://hal.inria.fr/hal-02151135
  • 16N. Crasto, P. Weinzaepfel, K. Alahari, C. Schmid.

    MARS: Motion-Augmented RGB Stream for Action Recognition, in: CVPR 2019 - IEEE Conference on Computer Vision & Pattern Recognition, Long Beach, CA, United States, IEEE, June 2019, pp. 1-10.

    https://hal.inria.fr/hal-02140558
  • 17N. Dvornik, C. Schmid, J. Mairal.

    Diversity with Cooperation: Ensemble Methods for Few-Shot Classification, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, pp. 1-12, https://arxiv.org/abs/1903.11341 - Added experiments with different network architectures and input image resolutions.

    https://hal.archives-ouvertes.fr/hal-02080004
  • 18M. Elbayad, J. Gu, E. Grave, M. Auli.

    Depth-adaptive Transformer, in: ICLR 2020 - Eighth International Conference on Learning Representations, Addis Ababa, Ethiopia, December 2019, pp. 1-14.

    https://hal.inria.fr/hal-02422914
  • 19V. Gabeur, J.-S. Franco, X. Martin, C. Schmid, G. Rogez.

    Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, pp. 1-10.

    https://hal.inria.fr/hal-02242795
  • 20Y. Hasson, G. Varol, D. Tzionas, I. Kalevatykh, M. J. Black, I. Laptev, C. Schmid.

    Learning joint reconstruction of hands and manipulated objects, in: CVPR 2019 - IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, United States, IEEE, June 2019, pp. 1-14.

    https://hal.archives-ouvertes.fr/hal-02429093
  • 21R. Klokov, J. Verbeek, E. Boyer.

    Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image, in: BMVC 2019 - British Machine Vision Conference, Cardiff, United Kingdom, September 2019, pp. 1-15, https://arxiv.org/abs/1908.07475 - Awarded with Best Science Paper Honourable Mention Award at BMVC'19..

    https://hal.inria.fr/hal-02268466
  • 22A. Kulunchakov, J. Mairal.

    A Generic Acceleration Framework for Stochastic Composite Optimization, in: NeurIPS 2019 - Thirty-third Conference Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-24, https://arxiv.org/abs/1906.01164.

    https://hal.inria.fr/hal-02139489
  • 23A. Kulunchakov, J. Mairal.

    Estimate Sequences for Variance-Reduced Stochastic Composite Optimization, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, June 2019, pp. 1-24, https://arxiv.org/abs/1905.02374 - short version of preprint arXiv:1901.08788.

    https://hal.inria.fr/hal-02121913
  • 24T. Lucas, K. Shmelkov, K. Alahari, C. Schmid, J. Verbeek.

    Adaptive Density Estimation for Generative Models, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-24.

    https://hal.archives-ouvertes.fr/hal-01886285
  • 25A. Pashevich, R. Strudel, I. Kalevatykh, I. Laptev, C. Schmid.

    Learning to Augment Synthetic Images for Sim2Real Policy Transfer, in: IROS 2019 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Macao, China, November 2019, pp. 1-6, https://arxiv.org/abs/1903.07740 - 7 pages.

    https://hal.archives-ouvertes.fr/hal-02273326
  • 26J. Peyre, I. Laptev, C. Schmid, J. Sivic.

    Detecting unseen visual relations using analogies, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, https://arxiv.org/abs/1812.05736v3.

    https://hal.archives-ouvertes.fr/hal-01975760
  • 27A. Ruiz, J. Verbeek.

    Adaptative Inference Cost With Convolutional Neural Mixture Models, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, pp. 1-12.

    https://hal.archives-ouvertes.fr/hal-02267564
  • 28A. Sablayrolles, M. Douze, Y. Ollivier, C. Schmid, H. Jégou.

    White-box vs Black-box: Bayes Optimal Strategies for Membership Inference, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, June 2019, https://arxiv.org/abs/1908.11229.

    https://hal.inria.fr/hal-02278902
  • 29A. Sablayrolles, M. Douze, C. Schmid, H. Jégou.

    Spreading vectors for similarity search, in: ICLR 2019 - 7th International Conference on Learning Representations, New Orleans, United States, May 2019, pp. 1-13, https://arxiv.org/abs/1806.03198 - Published at ICLR 2019.

    https://hal.inria.fr/hal-02278905
  • 30V. Sydorov, K. Alahari, C. Schmid.

    Focused Attention for Action Recognition, in: BMVC 2019 - British Machine Vision Conference, Cardiff, United Kingdom, September 2019, pp. 1-12.

    https://hal.archives-ouvertes.fr/hal-02292339
  • 31M. Vladimirova, J. Verbeek, P. Mesejo, J. Arbel.

    Understanding Priors in Bayesian Neural Networks at the Unit Level, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, Proceedings of the 36th International Conference on Machine Learning, June 2019, vol. 97, pp. 6458-6467, https://arxiv.org/abs/1810.05193 - 10 pages, 5 figures, ICML'19 conference. [ DOI : 10.05193 ]

    https://hal.archives-ouvertes.fr/hal-02177151

Conferences without Proceedings

  • 32A. Ruiz, O. Martinez, X. Binefa, J. Verbeek.

    Learning Disentangled Representations with Reference-Based Variational Autoencoders, in: ICLR workshop on Learning from Limited Labeled Data, New Orleans, United States, May 2019, pp. 1-17.

    https://hal.inria.fr/hal-01896007

Other Publications



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