1. [Siahkoohi et al, 2023] Unearthing InSights into Mars: unsupervised source separation with limited data, Siahkoohi A., Morel R., de Hoop M., Allys E., Sainton G. and Kawamura T., 2023. (PDF)
  2. [Arnaboldi et al, 2023] From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks, Arnaboldi L., Stephan L., Krzakala F. and Loureiro B., 2023. (PDF)
  3. [Schröder et al, 2023] Deterministic equivalent and error universality of deep random features learning, Schröder D., Cui H. Dmitriev D. and Loureiro B., 2023. (PDF)
  4. [Clarté et al, 2023] Expectation consistency for calibration of neural networks, Clarté L., Loureiro B., Krzakala F., and Zdeborovà L., 2023. (PDF)
  5. [Gerace et al, 2023] Gaussian Universality of Perceptrons with Random Labels, Gerace F., Krzakala F., Loureiro B., Stephan L. and Zdeborovà L., 2023. (PDF)
  6. [Dandi et al, 2023] Universality laws for Gaussian mixtures in generalized linear models, Dandi Y., Stephan L., Krzakala F., Loureiro B. and Zdeborovà L., 2023. (PDF)
  7. [Pesce et al, 2023] Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation, Pesce L., Krzakala F., Loureiro B. and Stephan L., 2023. (PDF)
  8. [Kadkhodaie et al, 2023] Learning multi-scale local conditional probability models of images, Kadkhodaie Z., Guth F., Mallat S. and Simoncelli E., International Conference on Learning Representations 2023, May. 2023. (PDF)
  9. [Mallat et al, 2023] Chapter Wavelet Phase Harmonics in Theoretical Physics, Wavelets, Analysis, Genomics An Indisciplinary Tribute to Alex Grossmann (Flandrin P., Jaffard S., Paul T.,Torresani B.), Mallat S., Rochette G., Zhang S., 2023. (PDF)
  10. [Deligiannidis et al, 2023] Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure, Deligiannidis G., de Bortoli V. and Doucet A., 2023. (PDF)
  11. [Heng et al, 2023] Simulating Diffusion Bridges with Score Matching, Heng J., de Bortoli V., Doucet A. and Thornton J., 2023. (PDF)
  12. [Crucinio et al, 2023] Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows, Crucinio F., Bortoli V., Doucet A. and Johansen A., 2023. (PDF)
  13. [Noble et al, 2023] Barrier Hamiltonian Monte Carlo, Noble M., de Bortoli V. and Durmus A., 2023. (PDF)
  14. [Noble et al, 2023] From Denoising Diffusions to Denoising Markov Models, Noble M., de Bortoli V. and Durmus A., 2023. (PDF)
  15. 2022

  16. [Pesce et al, 2022] Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap, Pesce L., Krzakala F., Loureiro B. and Zdeborovà L., Neural Information Processing Systems conference 2022, 2022. (PDF)
  17. [Marchand et al, 2022] Wavelet Conditional Renormalization Group, Marchand T., Ozawa M., Biroli G., Mallat S., 2022. (PDF)
  18. [de Bortoli and Desolneux, 2022] On quantitative Laplace-type convergence results for some exponential probability measures, with two applications, de Bortoli V. and Desolneux A., Journal of Machine Learning Research, 2022. (PDF)
  19. [Brochard et al, 2022] Particle gradient descent model for point process generation, Brochard A., Błaszczyszyn B., Zhang S. and Mallat S., Statistics and Computing, 2022. (PDF)
  20. [Pesce et al, 2022] Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap, Pesce L., Loureiro B., Krzakala F. and Zdeborovà L., Neural Information Processing Systems conference 2022, 2022. (PDF)
  21. [Thornton et al, 2022] Riemannian Diffusion Schrödinger Bridge, Thornton J., Hutchinson M., Mathieu E., de Bortoli V., Teh Y. and Doucet A., International Conference on Machine Learning 2022, 2022. (PDF)
  22. [de Bortoli et al, 2022] Riemannian Score-Based Generative Modelling, de Bortoli V., Mathieu E.,Hutchinson M., Thornton J., Teh Y. and Doucet A., Neural Information Processing Systems conference 2022, 2022. (PDF)
  23. [Shi et al, 2022] Conditional Simulation Using Diffusion Schrödinger Bridges, Shi Y., de Bortoli V., Deligiannidis G. and Doucet A., Uncertainty in Artificial Intelligence conference 2022, 2022. (PDF)
  24. [Campbell et al, 2022] A Continuous Time Framework for Discrete Denoising Models, Campbell A., Benton J., de Bortoli V.,Rainforth T., Deligiannidis G. and Doucet A., Neural Information Processing Systems conference 2022, 2022. (PDF)
  25. [Guth et al, 2022] Wavelet Score-Based Generative Modeling, Guth F., Coste S., de Bortoli V. and Mallat S., Neural Information Processing Systems conference 2022, 2022. (PDF)
  26. [Phillips et al, 2022] Spectral Diffusion Processes, Phillips A., Seror T., Hutchinson M., de Bortoli V., Doucet A. and Mathieu E., Neural Information Processing Systems conference 2022, 2022. (PDF)
  27. [Nguyen et al, 2022] An Online Minorization-Maximization Algorithm, Nguyen H. D., Forbes F., Fort G. and Cappé O., ICFS (17th conference of the International Federation of Classification Societies), 2022. (PDF)
  28. [Zhang, 2022] On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes, Zhang S., 2022. (PDF)
  29. [Brochard et al, 2022] Generalized rectifier wavelet covariance models for texture synthesis, Brochard A., Zhang S. and Mallat S., International Conference on Learning Representations 2022, 2022. (PDF)
  30. [Parikh et al, 2022] An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models, Parikh R., Rochette G., Espy-Wilson C. and Shamma S., Interspeech Conference 2022, 2022. (PDF)
  31. [Guth et al, 2022] Phase Collapse in Neural Networks, Guth F., Zarka J. and Mallat S., International Conference on Learning Representations, 20222, 2022. (PDF)
  32. [Clarté et al, 2022] A study of uncertainty quantification in overparametrized high-dimensional models, Clarté L., Loureiro B., Krzakala F. and Zdeborovà, L., 2022. (PDF)
  33. [Veiga et al, 2022] Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks, Veiga R., Stephan L., Loureiro B., Krzakala F. and Zdeborovà, L., 2022. (PDF)
  34. [Eickenberg et al, 2022] Wavelet Moments for Cosmological Parameter Estimation, Eickenberg M., Allys E., Dizgah A., Lemos P., Massara E., Abidi M., Hahn C., Hassan S., Régaldo-Saint Blancard B., Ho S., Mallat S., Anden J. and Villaescusa-Navarro F., 2022. (PDF)
  35. [Morel et al, 2022] Scale Dependencies and Self-Similarity Through Wavelet Scattering Covariance, Morel R., Rochette G., Leonarduzzi R., Bouchaud J.P. and Mallat S., 2022. (PDF)
  36. [Achddou et al, 2022] Regret Analysis of the Stochastic Direct Search Method for Blind Resource Allocation, Achddou J., Cappé O. and Garivier A., 2022. (PDF)
  37. [Achddou 2022] Optimisation d ordre zéro pour les enchères en temps réel : un point de vue mathématique, Achddou J., 2022. (PDF)
  38. [Russac 2022] Problèmes de décision séquentielle dans des environnements non-stationnaires, Russac Y., 2022. (PDF)
  39. 2021

  40. [Thiry et al. 2021] The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods, Louis THIRY and Michael Arbel and Eugene Belilovsky and Edouard Oyallon, International Conference on Learning Representations, Mai 2021. (PDF)
  41. [Kerdreux et al. 2021] Neural wild guess, Louis Thiry Thomas Kerdreux, CVPR workshop Ethical Considerations in Creative applications of Computer Vision, June 2021. (PDF)
  42. [Zhang and Mallat, 2021] Maximum entropy models from phase harmonic covariances, Zhang S. and Mallat S., Applied and Computational Harmonic Analysis, 2021. (PDF)
  43. [de Bortoli et al, 2021] Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling, de Bortoli V., Thornton J., Heng J. and Doucet A., Advances in Neural Information Processing Systems, 2021. (PDF)
  44. [Zarka et al, 2021] Separation and Concentration in Deep Networks, Zarka J., Guth F. and Mallat S., ICLR 2021 - 9th International Conference on Learning Representations, 2021. (PDF)
  45. [Thiry, 2021] Efficacité des méthodes locales pour la classification d images et la regression d énergie en physique, Thiry L., 2021. (PDF)
  46. [Kirkpatrick et al, 2021] Pushing the frontiers of density functionals by solving the fractional electron problem, Kirkpatrick J., McMorrow B., Turban D., Gaunt A., Spencer J., Matthews A., Obika A., Thiry L., Fortunato M., Pfau D., Castellanos L., Petersen S., Nelson A., Kohli P., Mori-Sánchez P., Hassabis D., Cohen J., Science, 2021. (PDF)
  47. 2020

  48. [Lapointe et al. 2020] Machine learning surrogate models for prediction of point defect vibrational entropy, Lapointe, Clovis and Swinburne, Thomas D and Thiry, Louis and Mallat, St{'e}phane and Proville, Laurent and Becquart, Charlotte S and Marinica, Mihai-Cosmin, Phys. Rev. Materials, March 2020. (PDF)
  49. [Kerdreux et al., 2020] Interactive Neural style transfer with artists, Kerdreux, T. and Thiry, L. and Kerdreux, E., International conference of computational creativity 2020, Coimbre, Portugal, September 2020. (PDF)
  50. [Zarka et al., 2020] Deep Network Classification by Scattering and Homotopy Dictionary Learning, Zarka J., Thiry L., Angles T., and Mallat S., Eighth International Conference on Learning Representations (ICLR 2020), April 2020. (PDF)
  51. [Kymatio] Kymatio: Scattering Transforms in Python, Andreux, M. and Angles, T. and Exarchakis, G. and Leonarduzzi, R. and Rochette, G. and Thiry, L. and Zarka, J. and Mallat, S. and Anden, J. and Belilovsky, E. and Bruna, J. and Lostanlen, V. and Chaudhary, M. and Hirn, Matthew J. and Oyallon, Edouard and Zhang, S. and Cella, C. and Eickenberg, Michael, Journal of Machine Learning Research 21 (2020) 1-6, January 2020. (PDF)
  52. [Allys et al, 2020] New Interpretable Statistics for Large Scale Structure Analysis and Generation, Allys E., Marchand T., Cardoso J.-F, Villaescusa-Navarro F., Ho S., and Mallat S., Phys.Rev.D, 2020. (PDF)
  53. [Zhang et al, 2020] Leveraging Joint-Diagonalization in Transform-Learning NMF, Zhang S., Soubies E. and Févotte C., IEEE Transactions on Signal Processing, 2020. (PDF)
  54. [Rochette et al, 2020] Efficient Per-Example Gradient Computations in Convolutional Neural Networks, Rochette G., Manoel A. and Tramel E., Workshop on Theory and Practice of Differential Privacy (TPDP), 2020. (PDF)
  55. 2019

  56. [Cabannes et al., 2019] Dialog on a Canvas with a Machine, Cabannes, V. and Kerdreux, T. and Thiry, L. and Campana, T. and Ferrandes, C., Machine Learning for Creativity and Design, NeurIPS 2019 Workshop, Vancouver, Canada, December 2019. (PDF)
  57. [Brochard et al., 2019] Statistical learning of geometric characteristics of wireless networks, Brochard, A., Błaszczyszyn, B., Mallat, S., and Zhang, S., Proc. of IEEE INFOCOM 2019: 2224-2232, 2019. (PDF)
  58. [Leonarduzzi et al., 2019] Maximum-entropy Scattering Models for Financial Time Series, Leonarduzzi, R., Rochette, G., Bouchaud, J-P., and Mallat, S., Proc. of IEEE ICASSP 2019: 5496-5500, 2019. (PDF)
  59. [Mallat et al., 2019] Phase Harmonic Correlations and Convolutional Neural Networks, Mallat, S., Zhang, S., and Rochette, G., IMA Journal of Information and Inference, November 2019. (PDF)
  60. [Zhang et Mallat, 2019] Maximum Entropy Models from Phase Harmonic Covariances, Zhang, S., and Mallat, S., arXiv:1911.10017, November 2019. (PDF)
  61. [Anden et al., 2019] Classification with joint time-frequency scattering, Anden, J., Lostanlen, V., and Mallat, S., IEEE Trans. on Signal Processing, vol 17, no. 4, May 2019. (PDF)
  62. [Allys et al, 2019] The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM, Allys E., Boulanger F., Levrier F., Zhang S., Colling C., Regaldo-Saint Blancard B., Hennebelle P. and Mallat S., Astronomy and Astrophysics - A&A, 2019. (PDF)
  63. 2018

  64. [Bruna et Mallat, 2018] Multiscale Sparse Microcanonical Models, Bruna, J. and Mallat, S., Jour. of Math. Stat. and Learning, vol. 1, no. 3, p. 257-315, November 2018. (PDF)
  65. [Andreux et Mallat, 2018] Music generation and transformation with moment matching scattering inverse networks, Andreux, M. and Mallat, S., 19th International Society for Music Information Retrieval Conference (ISMIR 2018), September 2018. (PDF)
  66. [Angles et Mallat, 2018] Generative Networks as Inverse Problems with Scattering Transforms, Angles, T. and Mallat, S., Sixth International Conference on Learning Representations (ICLR 2018), May 2018. (PDF)
  67. [Eickenberg et al., 2018] Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties, Eickenberg, M., Exarchakis, G., Hirn, M., Mallat, S., and Thiry, L., Jour. of Chemical Physics, vol 148, no. 24, May 2018. (PDF)
  68. [Jacobsen et al, 2018] i-RevNet: Deep Invertible Networks, Jacobsen J., Smeulders A., Oyallon E., ICLR 2018-International Conference on Learning Representations, 2018. (PDF)
  69. 2017

  70. [Eickenberg et al., 2017] Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities, Eickenberg, M., Exarchakis, G., Hirn, M., and Mallat, S., Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017. (PDF)
  71. [Oyallon, 2017b] Analyzing and Introducing Structures in Deep Convolutional Neural Networks, Oyallon, E., Ph.D. thesis, École normale supérieure - PSL Research University, Paris, France, Oct. 2017. (PDF)
  72. [Oyallon et al., 2017] Scaling the Scattering Transform: Deep Hybrid Networks, Oyallon, E., Belilovsky, E., and Zagoruyko, S., ICCV, 2017. (PDF - v1) (PDF - v2)
  73. [Jacobsen et al., 2017] Hierarchical Attribute CNNs, Jacobsen, J.-H., Oyallon, E., Mallat, S. and Smeulders, A.W.M., ICML PADL, 2017. (PDF)
  74. [Oyallon, 2017a] Building a Regular Decision Boundary with Deep Networks, Oyallon, E., CVPR, 2017. (PDF)
  75. [Lostanlen, 2017] Convolutional Operators in the Time-frequency Domain, Lostanlen V., Ph.D. thesis, École normale supérieure - PSL Research University, Paris, France, Feb. 2017. (PDF)
  76. 2016

  77. [Lostanlen and Cella, 2016] Deep convolutional networks on the pitch spiral for music instrument recognition, Lostanlen V., Cella C.-E., Proc. ISMIR, New York, USA, August 2016. (PDF) (Code)
  78. [Dong et al., 2016] Reservoir Computing with light scattering, Dong J., Gigan S., Krzakala F., Wainrib G., Submitted, 2016.
  79. [Wainrib and Amar, 2016] Iterative random autoencoders and the ping-pong algorithm, Wainrib G., Amar G., Submitted, 2016.
  80. [Morilla et al., 2016] Colonic microRNA-based composite algorithm predicting drug responses in acute severe ulcerative colitis, Morilla I., Uzzan M., Laharie D., Cazals-Hatem D., Daniel F., Bouhnik Y., Ogier-Denis E., Wainrib G., Treton X., Submitted, 2016.
  81. [Ding et al., 2016] Ulcerative Colitis and smoking: an integrative network-based analysis of detoxification gene expression data, Ding Y.-P., Ladeiro Y., Bouhnik Y., Marah A., Zaag H., Cazals-Hatem D., Seksik P., Daniel F., Hugot J.-P., Morilla I., Wainrib G., Tréton X., Ogier-Denis E., Submitted, 2016.
  82. [Victor et al., 2016] Network modeling of Crohn's disease, Victor J.-M., Debret G., Lesne A., Pascoe L., Carrivain P., Wainrib G., Hugot J.-P., PLoS One (to appear), 2016.
  83. [Couillet et al., 2016c] Training performance of echo state neural networks, Couillet R., Wainrib G., Sevi H., Ali H.T., IEEE Statistical Signal Processing Workshop (SSP), Palma de Majorca, Spain, 2016. (PDF)
  84. [Wainrib, 16] Context-dependent representation in recurrent neural networks, Wainrib G., arXiv preprint arXiv:1506.06602, 2016. (PDF)
  85. [Couillet et al., 2016b] The asymptotic performance of linear echo state neural networks, Couillet R., Wainrib G., Sevi H., Ali H.T., Submitted, 2016.
  86. [Couillet et al., 2016a] A random matrix approach to echo-state neural networks, Couillet R., Wainrib G., Sevi H., Ali H.T., International Conference on Machine Learning (ICML), New York, June 2016. (PDF)
  87. [Milišić and Wainrib, 2016] Mathematical modeling of lymphocytes selection in the germinal center, Milišić V., Wainrib G., Journal of Mathematical Biology, 2016. (PDF)
  88. [Wainrib and Galtier, 2016] A local Echo State Property through the largest Lyapunov exponent, Wainrib G., Galtier M., Neural Networks, Apr. 2016. (PDF)
  89. [del Molino et al., 2016] The real Ginibre ensemble with k=O(n) real eigenvalues, del Molino LCG., Pakdaman K., Touboul J., Wainrib G., Journal of Statistical Physics, Mar. 2016. (PDF)
  90. 2015

  91. [Waldspurger, 2015b] Phase retrieval for wavelet transforms, Waldspurger I., arXiv preprint arXiv:1512.07024, 2015. (PDF)
  92. [Lostanlen and Mallat, 2015b] Wavelet Scattering on the Pitch Spiral, Lostanlen V. and Mallat S., Proceedings of DAFx-15, 2015. (PDF) (Code)
  93. [Waldspurger, 2015a] Wavelet transform modulus: phase retrieval and scattering, Waldspurger I., Ph.D. thesis, ED386, Paris, France, Nov. 2015. (PDF)
  94. [Andén et al., 2014] Joint Time-Frequency Scattering for Audio Classification, Andén J., Lostanlen V., Mallat S., Proceedings of 2015 IEEE MLSP Workshop, Sept. 2015. (PDF) (Code)
  95. [Lostanlen and Mallat, 2015a] Transformée en scattering sur la spirale temps-chroma-octave, Lostanlen V. and Mallat S., Proceedings of GRETSI 2015, 2015. (PDF) (Code)
  96. [Wainrib and Galtier, 2015] Regular graphs maximize the variability of random neural networks, Wainrib G. and Galtier M., Physical Review E, Sep. 2015. (PDF)
  97. [Touboul and Wainrib, 2015] Dynamics and absorption properties of stochastic equations with Hölder diffusion coefficients, Touboul J. and Wainrib G., Physica D: Nonlinear Phenomena, Jul. 2015. (PDF)
  98. [Oyallon and Mallat, 2015] Deep Roto-Translation Scattering for Object Classification, Oyallon E. and Mallat S., Proceedings in IEEE CVPR 2015 conference, 2015. (PDF) (Code)
  99. [Mallat and Waldspurger, 2015] Phase retrieval for the Cauchy wavelet transform, Mallat S. and Waldspurger I., Journal of Fourier Analysis and Applications, 2014. (PDF)
  100. [d'Aspremont et al., 2015] Phase Recovery, MaxCut and Complex Semidefinite Programming, d'Aspremont A., Mallat S., Waldspurger I., Mathematical Programming, 2015. (PDF)
  101. [Hirn et al., 2015] Quantum Energy Regression using Scattering Transforms, Hirn M., Poilvert N., Mallat S., arXiv preprint arXiv:1502.02077, 2015. (PDF)
  102. [Balelli et al., 2015] Branching random walks on binary strings and application to adaptative immunity, Balelli I., Milišić V., Wainrib G., arXiv preprint arXiv:1501.07806, 2015. (PDF)
  103. 2014

  104. [Oyallon et al., 2014] Generic Deep Networks with wavelet Scattering, Oyallon, E., Mallat, S. and Sifre, L., ICLR workshop, 2014. (PDF)
  105. [Chen et al., 2014] Unsupervised Deep Haar Scattering on Graphs, Chen X., Cheng X., Mallat S., Conference on Neural Information Processing Systems (NIPS), Montreal, Quebec, Canada, Dec. 2014. (PDF) (Code)
  106. [Sifre, 2014] Rigid-Motion Scattering for Image Classification, Sifre L., Ph.D. thesis, École polytechnique, Palaiseau, France, Oct. 2014. (PDF)
  107. [Wolf et al., 2014] Audio Source Separation with Time-Frequency Velocities, Wolf G., Mallat S., Shamma S., Proceedings of 2014 IEEE MLSP Workshop, Sept. 2014. (PDF)
  108. [Marini et al., 2014] Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes, Galtier M., Marini C., Wainrib G., Jaeger H., Neural Networks, Aug. 2014. (PDF)
  109. [Sifre and Mallat, 2014] Rigid-Motion Scattering for Texture Classification, Sifre L. and Mallat S., submitted to International Journal of Computer Vision, 2014. (PDF)
  110. [Talmon et al., 2014] Manifold Learning for Latent Variable Inference in Dynamical Systems, Talmon R., Mallat S., Zaveri H., Coifman R. R., Technical Report, 2014. (PDF)
  111. [Chudacek et al., 2014] Low Dimensional Manifold Embedding For Scattering Coefficients Of Intrapartum Fetale Heart Rate Variability, Chudacek V., Talmon R., Anden J., Mallat S., Coifman R. R., Abry P., Doret M., 2014 Internat. IEEE Conf. in Medicine and Biology , EMBC'14, 2014. (PDF)
  112. [Allez et al., 2014] Index Distribution of the Ginibre Ensemble, Allez R., Touboul J., Wainrib G., Journal of Physics A: Mathematical and Theoretical, Jan. 2014. (PDF)
  113. 2013

  114. [Bruna and Mallat, 2013a] Scattering transform for image classification:
    Invariant Scattering Convolution Network, Bruna J. and Mallat S., IEEE Trans. on PAMI, vol. 35, no. 8, pp. 1872-1886, Aug. 2013. (PDF)
  115. [Sifre and Mallat, 2013] Affine invariant scattering for texture classification (8 pages):
    Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination, Sifre L. and Mallat S., Proceedings in IEEE CVPR 2013 conference, 2013. (PDF) (Code)
  116. [Bruna et al., 2013] Intermittent Process Analysis with Scattering Moments, Bruna J., Mallat S., Bacry E. and Muzy J.-F., arXiv preprint arXiv:1311.4104, 2013. (PDF)
  117. [Bruna and Mallat, 2013b] Audio texture synthesis with scattering moments, Bruna J. and Mallat S., arXiv preprint arXiv:1311.0407, 2013. (PDF)
  118. [Hammari et al., 2013] Wavelet methods for shape perception in electro-sensing, Hammari H., Mallat S., Waldspurger I., Wang H., arXiv preprint arXiv:1310.2842, 2013. (PDF)
  119. [Fogel et al., 2013] Phase retrieval for imaging problems, Fogel F., Waldspurger I., d'Aspremont A., arXiv preprint arXiv:1304.7735, 2013. (PDF)
  120. 2012

  121. [Andén and Mallat, 2011b] Audio scattering transform, scattering of parametrized models, transposition invariance, and classification results on musical genre identification and phone classification (31 pages):
    Deep Scattering Spectrum, Andén J. and Mallat. S., Submitted to IEEE Transactions on Signal Processing, 2011. (PDF)
  122. [Mallat, 2012] Mathematical introduction of scattering operators for translation and rotation invariant representations:
    Group Invariant Scattering, Mallat S., Communications in Pure and Applied Mathematics, vol. 65, no. 10, pp. 1331-1398, Oct. 2012. (PDF)
  123. [Sifre and Mallat, 2012] Scattering along spatial and angular variable for rotation invariance (6 pages):
    Combined Scattering for Rotation Invariant Texture Analysis, Sifre L. and Mallat S., Proceedings of the ESANN 2012 conference, Apr. 2012. (PDF)
  124. [Andén and Mallat, 2012] Modulated source-filter models and their representation in the scattering transform (6 pages):
    Scattering Representation of Modulated Sounds, Andén J. and Mallat S., Proceedings of the DAFx 2012 conference, 2012. (PDF)
  125. [Bruna, 2012] PhD thesis with extensive scattering review, new mathematical properties, applications on pattern and texture classification, and multifractal scattering (200 pages):
    Scattering Representations for Recognition, Bruna J., Ph.D. thesis, École Polytechnique, Palaiseau, France, Nov. 2012. (PDF)
  126. 2011

  127. [Bruna and Mallat, 2011] Classification with Scattering Operators, Bruna J. and Mallat S., Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 1561-1566, 2011.
  128. [Andén and Mallat, 2011a] Scattering transform applied to audio signals and musical classification (6 pages):
    Multiscale Scattering for Audio Classification, Andén J. and Mallat S., Proceedings of the ISMIR 2011 conference, pp. 657-662, 2011. (PDF)
  129. 2010

  130. [Mallat, 2010] Recursive interferometric Representations, Mallat S., 18th European Signal Processing Conference (EUSIPCO-2010) Aalborg, Denmark, August 23-27, 2010. (PDF)