| Training deep neural networks using conjugate gradient-like methods H Iiduka, Y Kobayashi Electronics 9 (11), 1809, 2020 | 7 | 2020 |
| Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning Y Kobayashi, H Iiduka arXiv preprint arXiv:2003.00231, 2020 | 5 | 2020 |
| eCo-FEV: efficient Cooperative infrastructure for Fully Electric Vehicles L Lin, Y Kobayashi, M Lenardi, W Klaudel, H Inhuelsen, MP Bianconi, ... 20th ITS World CongressITS Japan, 2013 | 1 | 2013 |
| CONJUGATE-GRADIENT-BASED ADAM FOR NONCONVEX STOCHASTIC OPTIMIZATION AND ITS APPLICATION TO DEEP LEARNING Y Kobayashi, H Iiduka JOURNAL OF NONLINEAR AND CONVEX ANALYSIS 23 (2), 337-356, 2022 | | 2022 |
| Establishment of a New Safe Driving Indicator through the Recognition of Traffic Lights T Waki, H Oishi, J Tanaka, M Matsumiya, Y Kobayashi 20th ITS World CongressITS Japan, 2013 | | 2013 |
| Application of the Camera-Equipped Drive Recorder to the Recognition of Road Surface Markings K Muramatsu, T Kato, J Tanaka, H Oishi, M Matsumiya, Y Kobayashi 20th ITS World CongressITS Japan, 2013 | | 2013 |
| Automatic Recognition of the Number of Passengers to Revise a Bus Route to the Needs of Passengers M Matsumiya, J Tanaka, H Oishi, Y Kobayashi 20th ITS World CongressITS Japan, 2013 | | 2013 |
| INCREMENTAL PROXIMAL METHOD FOR NONSMOOTH CONVEX OPTIMIZATION WITH FIXED POINT CONSTRAINTS OF QUASI-NONEXPANSIVE MAPPINGS H OISHI, YU KOBAYASHI, H IIDUKA | | |
