Publications

Learning and Optimization: [3,4,7,8,9,10].

High dimensional data modeling and analysis: [1,2,5,6].

[1] A. Brochard, B. Błaszczyszyn, S. Mallat, and S. Zhang, “Particle gradient descent model for point process generation,” arXiv preprint arXiv:2010.14928, 2020, [Arxiv].

[2] S. Mallat, S. Zhang, and G. Rochette, “Phase harmonic correlations and convolutional neural networks,” Information and Inference: A Journal of the IMA, vol. 9, no. 3, pp. 721–747, 2020, [Online],[Arxiv].

[3] S. Zhang, E. Soubies, and C. Févotte, “On the Identifiability of Transform Learning for Non-negative Matrix Factorization,” IEEE Signal Processing Letters, vol. 27, pp. 1555–1559, 2020, [Online],[Hal].

[4] M. Andreux et al., “Kymatio: Scattering transforms in python,” Journal of Machine Learning Research, vol. 21, no. 60, pp. 1–6, 2020, [Online].

[5] S. Zhang and S. Mallat, “Maximum entropy models from phase harmonic covariances,” Applied and Computational Harmonic Analysis, vol. 53, pp. 199–230, 2021, [Online],[Arxiv].

[6] E. Allys et al., “The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM,” Astronomy & Astrophysics, vol. 629, p. A115, 2019, [Online],[Arxiv].

[7] A. Brochard, B. Blaszczyszyn, S. Mallat, and S. Zhang, “Statistical learning of geometric characteristics of wireless networks,” in IEEE Infocom, 2019, pp. 2224–2232, [Online],[Arxiv].

[8] S. Zhang, A. E. Choromanska, and Y. LeCun, “Deep learning with Elastic Averaging SGD,” in Advances in Neural Information Processing Systems, 2015, pp. 685–693, [Online].

[9] L. Wan, M. Zeiler, S. Zhang, Y. Le Cun, and R. Fergus, “Regularization of Neural Networks using DropConnect,” in International Conference on Machine Learning, 2013, vol. 28, no. 3, pp. 1058–1066, [Online].

[10] T. Schaul, S. Zhang, and Y. LeCun, “No more pesky learning rates,” in International Conference on Machine Learning, 2013, vol. 28, no. 3, pp. 343–351, [Online].

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

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