3 papers
cs.LG2022
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks
Kentaro Ohno, Sekitoshi Kanai, Yasutoshi Ida
Gate functions in recurrent models, such as an LSTM and GRU, play a central role in learning various time scales in modeling time series data by using a bounded activation function…
stat.ML2021
Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation
Kentaro Ohno, Atsutoshi Kumagai
Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to contr…
cs.CR2021
On the Effectiveness of Clone Detection for Detecting IoT-related Vulnerable Clones
Kentaro Ohno, Norihiro Yoshida, Wenqing Zhu +1
Since IoT systems provide services over the Internet, they must continue to operate safely even if malicious users attack them. Since the computational resources of edge devices co…