Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation. - Kentaro Ohno; Atsutoshi Kumagai (ICBK, 2021)

On the Effectiveness of Clone Detection for Detecting IoT-related Vulnerable Clones. - Kentaro Ohno; Norihiro Yoshida; Wenqing Zhu; Hiroaki Takada (CoRR, 2021)

Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks. - Kentaro Ohno; Sekitoshi Kanai; Yasutoshi Ida (AAAI, 2023)

A Quasi-Initial Solution Giving Method for Ising Machines by Controlling External Magnetic Field Coefficients. - Soma Kawakami; Kentaro Ohno; Dema Ba; Satoshi Yagi; Junji Teramoto; Nozomu Togawa (ICCE, 2023)

One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. - Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Kentaro Ohno; Yasutoshi Ida (ICML, 2023)

A Security Verification Framework of Cryptographic Protocols Using Machine Learning. - Kentaro Ohno; Misato Nakabayashi (CoRR, 2023)

Linearization via Ordering Variables in Binary Optimization for Ising Machines. - Kentaro Ohno; Nozomu Togawa (CoRR, 2023)

An Interaction Coefficient Control Method for Setting Initial Solutions to Ising Machines. - Soma Kawakami; Kentaro Ohno; Dema Ba; Satoshi Yagi; Junji Teramoto; Nozomu Togawa (ICCE, 2024)

Toward Practical Benchmarks of Ising Machines: A Case Study on the Quadratic Knapsack Problem. - Kentaro Ohno; Tatsuhiko Shirai; Nozomu Togawa (IEEE Access, 2024)

Giving a Quasi-Initial Solution to Ising Machines by Controlling External Magnetic Field Coefficients. - Soma Kawakami; Kentaro Ohno; Dema Ba; Satoshi Yagi; Junji Teramoto; Nozomu Togawa (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2024)

Linearizing Binary Optimization Problems Using Variable Posets for Ising Machines. - Kentaro Ohno; Nozomu Togawa (IEEE Trans. Emerg. Top. Comput., 2025)