Sixin Zhang

According to our database, Sixin Zhang authored at least 24 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Feature Representation Transferring to Lightweight Models via Perception Coherence.

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi, Serge Gratton, Thierry Giaccone

CoRR, May, 2025

Convolutional Rectangular Attention Module.

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi, Serge Gratton, Thierry Giaccone

CoRR, March, 2025

Local convergence of simultaneous min-max algorithms to differential equilibrium on Riemannian manifold.

Sixin Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System.

Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini

CoRR, 2024

Large Margin Discriminative Loss for Classification.

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi, Serge Gratton, Thierry Giaccone

CoRR, 2024

Local convergence of min-max algorithms to differentiable equilibrium on Riemannian manifold.

Sixin Zhang

CoRR, 2024

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification.

Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi, Sixin Zhang, Serge Gratton, Thierry Giaccone

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2022

Transform learning NMF.

Sixin Zhang

Dataset, May, 2022

Leveraging Joint-Diagonalization in Transform-Learning NMF.

Sixin Zhang, Emmanuel Soubies, Cédric Févotte

IEEE Trans. Signal Process., 2022

Particle gradient descent model for point process generation.

Antoine Brochard, Bartlomiej Blaszczyszyn, Sixin Zhang, Stéphane Mallat

Stat. Comput., 2022

On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes.

Sixin Zhang

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Generalized rectifier wavelet covariance models for texture synthesis.

Antoine Brochard, Sixin Zhang, Stéphane Mallat

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

On the Relationships between Transform-Learning NMF and Joint-Diagonalization.

Sixin Zhang, Emmanuel Soubies, Cédric Févotte

CoRR, 2021

2020

On the Identifiability of Transform Learning for Non-Negative Matrix Factorization.

Sixin Zhang, Emmanuel Soubies, Cédric Févotte

IEEE Signal Process. Lett., 2020

Kymatio: Scattering Transforms in Python.

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

J. Mach. Learn. Res., 2020

2019

Maximum Entropy Models from Phase Harmonic Covariances.

Sixin Zhang, Stéphane Mallat

CoRR, 2019

Statistical learning of geometric characteristics of wireless networks.

Antoine Brochard, Bartlomiej Blaszczyszyn, Stéphane Mallat, Sixin Zhang

Proceedings of the 2019 IEEE Conference on Computer Communications, 2019

2018

Kymatio: Scattering Transforms in Python.

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

CoRR, 2018

Phase Harmonics and Correlation Invariants in Convolutional Neural Networks.

Stéphane Mallat, Sixin Zhang, Gaspar Rochette

CoRR, 2018

2016

Distributed Stochastic Optimization for Deep Learning.

Sixin Zhang

PhD thesis, 2016

Distributed stochastic optimization for deep learning (thesis).

Sixin Zhang

CoRR, 2016

2015

Deep learning with Elastic Averaging SGD.

Sixin Zhang, Anna Choromanska, Yann LeCun

Proceedings of the 3rd International Conference on Learning Representations, 2015

2013

Regularization of Neural Networks using DropConnect.

Li Wan, Matthew D. Zeiler, Sixin Zhang, Yann LeCun, Rob Fergus

Proceedings of the 30th International Conference on Machine Learning, 2013

No more pesky learning rates.

Tom Schaul, Sixin Zhang, Yann LeCun

Proceedings of the 30th International Conference on Machine Learning, 2013