| 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 | 95 | 2019 |
| Concept bottleneck model with additional unsupervised concepts Y Sawada, K Nakamura IEEE Access 10, 41758-41765, 2022 | 77 | 2022 |
| Information processing system, information processing method, and program H Motomura, MS Kourkouss, Y Sawada, T Mori, M Tsuji US Patent 10,759,446, 2020 | 75 | 2020 |
| 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 | 52 | 2012 |
| 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 | 45 | 2019 |
| 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 | 40 | 2015 |
| Image generation apparatus, image generation method, storage medium, and processing method Y Kato, T Sato, Y Sawada US Patent 10,191,265, 2019 | 36 | 2019 |
| Conditional Generative Adversarial Networks for Inorganic Chemical Compositions Y Sawada, K Morikawa, M Fujii Chemistry Letters 50 (4), 623-626, 2021 | 25* | 2021 |
| 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 | 24 | 2023 |
| Transfer learning apparatus, transfer learning system, transfer learning method, and recording medium Y Sawada, K Kozuka US Patent 10,832,128, 2020 | 19 | 2020 |
| 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 | 17 | 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 | 16 | 2019 |
| Learning apparatus, identifying apparatus, learning and identifying system, and recording medium Y Sawada, T Nakada, Y Sato US Patent 11,023,806, 2021 | 14 | 2021 |
| 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 | 14 | 2012 |
| 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 | 13 | 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 | 13* | 2018 |
| 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 | 12 | 2016 |
| Improving RNN performance by modelling informative missingness with combined indicators FJ Rodenburg, Y Sawada, N Hayashi Applied Sciences 9 (8), 1623, 2019 | 10 | 2019 |
| Image generating apparatus and image generating method Y Kato, Y Sawada, Y Mukaigawa, T Funatomi, H Kubo US Patent 10,607,316, 2020 | 9 | 2020 |
| C-SENN: Contrastive self-explaining neural network Y Sawada, K Nakamura arXiv preprint arXiv:2206.09575, 2022 | 8 | 2022 |
Kazuki KozukaStanford University, PanasonicOverená e-mailová adresa na: jp.panasonic.com
