154 citations · 221 across the 4 of their papers we have counts for
4 papers
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
The notion of flat minima has played a key role in the generalization studies of deep learning models. However, existing definitions of the flatness are known to be sensitive to th…
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions
Yusuke Tsuzuku, Issei Sato
Data-agnostic quasi-imperceptible perturbations on inputs are known to degrade recognition accuracy of deep convolutional networks severely. This phenomenon is considered to be a p…
Variance-based Gradient Compression for Efficient Distributed Deep Learning
Yusuke Tsuzuku, Hiroto Imachi, Takuya Akiba
Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation wo…
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
High sensitivity of neural networks against malicious perturbations on inputs causes security concerns. To take a steady step towards robust classifiers, we aim to create neural ne…