Data Analysis Competition Platform for Educational Purposes: Lessons Learned and Future Challenges. - Yukino Baba; Tomoumi Takase; Kyohei Atarashi; Satoshi Oyama; Hisashi Kashima (AAAI, 2018)

Longer Distance Weight Prediction for Faster Training of Neural Networks. - Masahito Kurihara; Satoshi Oyama; Tomoumi Takase (SMC, 2018)

Why Does Large Batch Training Result in Poor Generalization? A Comprehensive Explanation and a Better Strategy from the Viewpoint of Stochastic Optimization. - Tomoumi Takase; Satoshi Oyama; Masahito Kurihara (Neural Comput., 2018)

Effective neural network training with adaptive learning rate based on training loss. - Tomoumi Takase; Satoshi Oyama; Masahito Kurihara (Neural Networks, 2018)

Evaluation of Stratified Validation in Neural Network Training with Imbalanced Data. - Tomoumi Takase; Satoshi Oyama; Masahito Kurihara (BigComp, 2019)

Difficulty-weighted learning: A novel curriculum-like approach based on difficult examples for neural network training. - Tomoumi Takase (Expert Syst. Appl., 2019)

Time-domain Mixup Source Data Augmentation of sEMGs for Motion Recognition towards Efficient Style Transfer Mapping. - Suguru Kanoga; Tomoumi Takase; Takayuki Hoshino; Hideki Asoh (EMBC, 2021)

Dynamic batch size tuning based on stopping criterion for neural network training. - Tomoumi Takase (Neurocomputing, 2021)

Self-paced data augmentation for training neural networks. - Tomoumi Takase; Ryo Karakida; Hideki Asoh (Neurocomputing, 2021)

A Collaborative Training Using Crowdsourcing and Neural Networks on Small and Difficult Image Classification Datasets. - Tomoumi Takase (SN Comput. Sci., 2022)

Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. - Ryo Karakida; Tomoumi Takase; Tomohiro Hayase; Kazuki Osawa (ICML, 2023)

Feature combination mixup: novel mixup method using feature combination for neural networks. - Tomoumi Takase (Neural Comput. Appl., 2023)

Affinity-Weighted RandAugment for Problem-Oriented Augmentation. - Yuna Park; Tomoumi Takase; Keisuke Kameyama; Masaki Onishi (IJCNN, 2024)

Optimal layer selection for latent data augmentation. - Tomoumi Takase; Ryo Karakida (Neural Networks, 2025)