Effective neural network training with adaptive learning rate based on training loss T Takase, S Oyama, M Kurihara Neural Networks 101, 68-78, 2018 | 146 | 2018 |
Dynamic batch size tuning based on stopping criterion for neural network training T Takase Neurocomputing 429, 1-11, 2021 | 31 | 2021 |
Self-paced data augmentation for training neural networks T Takase, R Karakida, H Asoh Neurocomputing 442, 296-306, 2021 | 19 | 2021 |
Understanding gradient regularization in deep learning: Efficient finite-difference computation and implicit bias R Karakida, T Takase, T Hayase, K Osawa International Conference on Machine Learning, 15809-15827, 2023 | 13 | 2023 |
Why does large batch training result in poor generalization? A comprehensive explanation and a better strategy from the viewpoint of stochastic optimization T Takase, S Oyama, M Kurihara Neural computation 30 (7), 2005-2023, 2018 | 13 | 2018 |
Data analysis competition platform for educational purposes: lessons learned and future challenges Y Baba, T Takase, K Atarashi, S Oyama, H Kashima Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 8 | 2018 |
Feature combination mixup: novel mixup method using feature combination for neural networks T Takase Neural Computing and Applications 35 (17), 12763-12774, 2023 | 4 | 2023 |
Time-domain mixup source data augmentation of semgs for motion recognition towards efficient style transfer mapping S Kanoga, T Takase, T Hoshino, H Asoh 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 4 | 2021 |
Evaluation of stratified validation in neural network training with imbalanced data T Takase, S Oyama, M Kurihara 2019 IEEE International Conference on Big Data and Smart Computing (BigComp …, 2019 | 4 | 2019 |
Difficulty-weighted learning: A novel curriculum-like approach based on difficult examples for neural network training T Takase Expert Systems with Applications 135, 83-89, 2019 | 1 | 2019 |
Optimal layer selection for latent data augmentation T Takase, R Karakida Neural Networks 181, 106753, 2025 | | 2025 |
Affinity-Weighted RandAugment for Problem-Oriented Augmentation Y Park, T Takase, K Kameyama, M Onishi 2024 International Joint Conference on Neural Networks (IJCNN), 1-8, 2024 | | 2024 |
A Collaborative Training Using Crowdsourcing and Neural Networks on Small and Difficult Image Classification Datasets T Takase SN Computer Science 3 (2), 178, 2022 | | 2022 |
Longer Distance Weight Prediction for Faster Training of Neural Networks T Takase, S Oyama, M Kurihara 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2018 | | 2018 |
ニューラルネットワークの効果的な訓練のための探索と収束の制御 高瀬朝海 北海道大学, 2018 | | 2018 |
形彫り放電加工の工具消耗における熱影響の調査 高瀬朝海, 国枝正典 精密工学会学術講演会講演論文集 2014 年度精密工学会春季大会, 1175-1176, 2014 | | 2014 |
形彫り放電加工の逆方向シミュレーションの揺動加工への適用 高瀬朝海, 国枝正典 電気加工学会全国大会講演論文集 2012, 37-40, 2012 | | 2012 |
D06 逆方向シミュレーションを用いた揺動放電加工の軌跡の導出 (OS8 電気加工 (2)) 高瀬朝海, 国枝正典 生産加工・工作機械部門講演会: 生産と加工に関する学術講演会 2012.9, 199-202, 2012 | | 2012 |
揺動放電加工の逆方向シミュレーションの試み 高瀬朝海, 国枝正典 精密工学会学術講演会講演論文集 2012 (0), 127-128, 2012 | | 2012 |