no code implementations • ICML 2020 • Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
However, existing definitions of the flatness are known to be sensitive to the rescaling of parameters.
no code implementations • CVPR 2019 • Yusuke Tsuzuku, Issei Sato
Data-agnostic quasi-imperceptible perturbations on inputs are known to degrade recognition accuracy of deep convolutional networks severely.
no code implementations • ICLR 2018 • Yusuke Tsuzuku, Hiroto Imachi, Takuya Akiba
We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.
2 code implementations • NeurIPS 2018 • Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
High sensitivity of neural networks against malicious perturbations on inputs causes security concerns.
