Publications

Google Scholar: https://scholar.google.com/citations?user=-cL9xWMAAAAJ&hl=en&oi=ao

  1. Brochard, A., Zhang, S., Mallat, S.: Generalized rectifier wavelet covariance models for texture synthesis. In: International Conference on Learning Representations (2022)
  2. Zhang, S., Soubies, E., Févotte, C.: Leveraging Joint-Diagonalization in Transform-Learning NMF. IEEE Trans. Signal Process. (2022)
  3. Brochard, A., Błaszczyszyn, B., Mallat, S., Zhang, S.: Particle gradient descent model for point process generation. Stat. Comput. 32, (2022)
  4. Zhang, S.: On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes. In: Mathematical and Scientific Machine Learning (2022)
  5. Zhang, S., Mallat, S.: Maximum entropy models from phase harmonic covariances. Appl. Comput. Harmon. Anal. 53, 199–230 (2021)
  6. Mallat, S., Zhang, S., Rochette, G.: Phase harmonic correlations and convolutional neural networks. Inf. Inference A J. IMA. 9, 721–747 (2020)
  7. Zhang, S., Soubies, E., Févotte, C.: On the identifiability of transform learning for non-negative matrix factorization. IEEE Signal Process. Lett. 27, 1555–1559 (2020)
  8. Andreux, M., Angles, T., Exarchakis, G., Leonarduzzi, R., Rochette, G., Thiry, L., Zarka, J., Mallat, S., Andén, J., Belilovsky, E., Bruna, J., Lostanlen, V., Chaudhary, M., Hirn, M.J., Oyallon, E., Zhang, S., Cella, C., Eickenberg, M.: Kymatio: Scattering transforms in python. J. Mach. Learn. Res. 21, 1–6 (2020)
  9. Allys, E., Levrier, F., Zhang, S., Colling, C., Blancard, B.R.-S., Boulanger, F., Hennebelle, P., Mallat, S.: The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM. Astron. Astrophys. 629, A115 (2019)
  10. Brochard, A., Blaszczyszyn, B., Mallat, S., Zhang, S.: Statistical learning of geometric characteristics of wireless networks. In: IEEE Infocom. pp. 2224–2232. , Paris, France (2019)
  11. Zhang, S., Choromanska, A.E., LeCun, Y.: Deep learning with Elastic Averaging SGD. In: Advances in Neural Information Processing Systems. pp. 685–693. , Montreal, Canada (2015)
  12. Wan, L., Zeiler, M., Zhang, S., Cun, Y. Le, Fergus, R.: Regularization of Neural Networks using DropConnect. In: International Conference on Machine Learning. pp. 1058–1066. , Atlanta, Georgia, USA (2013)
  13. Schaul, T., Zhang, S., LeCun, Y.: No more pesky learning rates. In: International Conference on Machine Learning. pp. 343–351. , Atlanta, Georgia, USA (2013)
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