Conference Papers (Reviewed)

Yusuke Tsuzuku and Issei Sato. "On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions." CVPR (2019)

Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama. "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks." NeurIPS (2018)

Yusuke Tsuzuku, Hiroto Imachi, and Takuya Akiba. "Variance-based Gradient Compression for Efficient Distributed Deep Learning." ICLR Workshop (2018)

Preprints

Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama. "Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis." CoRR abs/1901.04653 (2019)