Yoshihide Sawada
Yoshihide Sawada
Other names澤田好秀
Unknown affiliation
Verified email at - Homepage
Cited by
Cited by
Data-driven approach to encoding and decoding 3-d crystal structures
J Hoffmann, L Maestrati, Y Sawada, J Tang, JM Sellier, Y Bengio
arXiv preprint arXiv:1909.00949, 2019
Information processing system, information processing method, and program
H Motomura, MS Kourkouss, Y Sawada, T Mori, M Tsuji
US Patent 10,759,446, 2020
Concept bottleneck model with additional unsupervised concepts
Y Sawada, K Nakamura
IEEE Access 10, 41758-41765, 2022
Robust nonrigid ICP using outlier-sparsity regularization
H Hontani, T Matsuno, Y Sawada
2012 IEEE Conference on Computer Vision and Pattern Recognition, 174-181, 2012
Transfer learning method using multi-prediction deep Boltzmann machines for a small scale dataset
Y Sawada, K Kozuka
2015 14th IAPR International Conference on Machine Vision Applications (MVA …, 2015
Driving assistance method, driving assistance device which utilizes same, autonomous driving control device, vehicle, driving assistance system, and program
K Emura, H Motomura, S Kourkouss, Y Sawada, M Tsuji, T Mori
US Patent App. 16/084,585, 2019
Image generation apparatus, image generation method, storage medium, and processing method
Y Kato, T Sato, Y Sawada
US Patent 10,191,265, 2019
Conditional Generative Adversarial Networks for Inorganic Chemical Compositions
Y Sawada, K Morikawa, M Fujii
Chemistry Letters 50 (4), 623-626, 2021
All-Transfer Learning for Deep Neural Networks and its Application to Sepsis Classification
Y Sawada, Y Sato, T Nakada, K Ujimoto, N Hayashi
22nd European Conference on Artificial Intelligence (ECAI2016), 1586-1587, 2016
Improvement in classification performance based on target vector modification for all-transfer deep learning
Y Sawada, Y Sato, T Nakada, S Yamaguchi, K Ujimoto, N Hayashi
Applied Sciences 9 (1), 128, 2019
S3NN: Time step reduction of spiking surrogate gradients for training energy efficient single-step spiking neural networks
K Suetake, S Ikegawa, R Saiin, Y Sawada
Neural Networks 159, 208-219, 2023
A study on graphical model structure for representing statistical shape model of point distribution model
Y Sawada, H Hontani
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012: 15th …, 2012
Whole layers transfer learning of deep neural networks for a small scale dataset
Y Sawada, K Kozuka
International Journal of Machine Learning and Computing 6 (1), 27, 2016
Learning apparatus, identifying apparatus, learning and identifying system, and recording medium
Y Sawada, T Nakada, Y Sato
US Patent 11,023,806, 2021
Rethinking the role of normalization and residual blocks for spiking neural networks
S Ikegawa, R Saiin, Y Sawada, N Natori
Sensors 22 (8), 2876, 2022
Disentangling Controllable and Uncontrollable Factors by Interacting with the World
Y Sawada, L Rigazio, K Morikawa, M Iwasaki, Y Bengio
Deep RL Workshop NeurIPS 2018, 2018
Image generating apparatus and image generating method
Y Kato, Y Sawada, Y Mukaigawa, T Funatomi, H Kubo
US Patent 10,607,316, 2020
Improving RNN performance by modelling informative missingness with combined indicators
FJ Rodenburg, Y Sawada, N Hayashi
Applied Sciences 9 (8), 1623, 2019
Image generation device, image generation method, recording medium, and method for generating an in-focus image based on feature points
Y Kato, T Sato, Y Sawada
US Patent 10,182,185, 2019
Accurate and robust registration of nonrigid surface using hierarchical statistical shape model
H Hontani, Y Tsunekawa, Y Sawada
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013
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