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2020 – today
- 2024
[i14]Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi, Sixin Zhang, Serge Gratton, Thierry Giaccone:
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification. CoRR abs/2404.08476 (2024)- [i13]Sixin Zhang:
Local convergence of min-max algorithms to differentiable equilibrium on Riemannian manifold. CoRR abs/2405.13392 (2024) - [i12]Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi, Serge Gratton, Thierry Giaccone:
Large Margin Discriminative Loss for Classification. CoRR abs/2405.18499 (2024) - [i11]Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini:
CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System. CoRR abs/2406.17788 (2024) - 2022
- [j4]Antoine Brochard
, Bartlomiej Blaszczyszyn, Sixin Zhang, Stéphane Mallat:
Particle gradient descent model for point process generation. Stat. Comput. 32(3): 49 (2022) - [j3]Sixin Zhang, Emmanuel Soubies, Cédric Févotte:
Leveraging Joint-Diagonalization in Transform-Learning NMF. IEEE Trans. Signal Process. 70: 3802-3817 (2022) - [c7]Antoine Brochard, Sixin Zhang, Stéphane Mallat:
Generalized rectifier wavelet covariance models for texture synthesis. ICLR 2022 - [c6]Sixin Zhang:
On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes. MSML 2022: 113-128 - [d1]Sixin Zhang:
Transform learning NMF. IEEE DataPort, 2022
- [i10]Sixin Zhang:
On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes. CoRR abs/2203.07136 (2022) - [i9]Antoine Brochard, Sixin Zhang, Stéphane Mallat:
Generalized Rectifier Wavelet Covariance Models For Texture Synthesis. CoRR abs/2203.07902 (2022) - 2021
- [i8]Sixin Zhang, Emmanuel Soubies, Cédric Févotte:
On the Relationships between Transform-Learning NMF and Joint-Diagonalization. CoRR abs/2112.05664 (2021) - 2020
- [j2]Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg:
Kymatio: Scattering Transforms in Python. J. Mach. Learn. Res. 21: 60:1-60:6 (2020) - [j1]Sixin Zhang, Emmanuel Soubies, Cédric Févotte:
On the Identifiability of Transform Learning for Non-Negative Matrix Factorization. IEEE Signal Process. Lett. 27: 1555-1559 (2020) - [i7]Antoine Brochard, Bartlomiej Blaszczyszyn, Stéphane Mallat, Sixin Zhang:
Particle gradient descent model for point process generation. CoRR abs/2010.14928 (2020)
2010 – 2019
- 2019
- [c5]Antoine Brochard, Bartlomiej Blaszczyszyn, Stéphane Mallat, Sixin Zhang:
Statistical learning of geometric characteristics of wireless networks. INFOCOM 2019: 2224-2232 - [i6]Sixin Zhang, Stéphane Mallat:
Maximum Entropy Models from Phase Harmonic Covariances. CoRR abs/1911.10017 (2019) - 2018
- [i5]Stéphane Mallat, Sixin Zhang, Gaspar Rochette:
Phase Harmonics and Correlation Invariants in Convolutional Neural Networks. CoRR abs/1810.12136 (2018) - [i4]Antoine Brochard, Bartlomiej Blaszczyszyn, Stéphane Mallat, Sixin Zhang:
Statistical learning of geometric characteristics of wireless networks. CoRR abs/1812.08265 (2018) - [i3]Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg:
Kymatio: Scattering Transforms in Python. CoRR abs/1812.11214 (2018) - 2016
- [b1]Sixin Zhang:
Distributed Stochastic Optimization for Deep Learning. New York University, USA, 2016 - [i2]Sixin Zhang:
Distributed stochastic optimization for deep learning (thesis). CoRR abs/1605.02216 (2016) - 2015
- [c4]Sixin Zhang, Anna Choromanska, Yann LeCun:
Deep learning with Elastic Averaging SGD. NIPS 2015: 685-693 - [c3]Sixin Zhang, Anna Choromanska, Yann LeCun:
Deep learning with Elastic Averaging SGD. ICLR (Workshop) 2015 - 2013
- [c2]Tom Schaul, Sixin Zhang, Yann LeCun:
No more pesky learning rates. ICML (3) 2013: 343-351 - [c1]Li Wan, Matthew D. Zeiler, Sixin Zhang, Yann LeCun, Rob Fergus:
Regularization of Neural Networks using DropConnect. ICML (3) 2013: 1058-1066 - 2012
- [i1]Tom Schaul, Sixin Zhang, Yann LeCun:
No More Pesky Learning Rates. CoRR abs/1206.1106 (2012)
Coauthor Index
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