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ORCIDTomoumi Takase
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2020 – today
- 2025
[j8]Tomoumi Takase
, Ryo Karakida:
Optimal layer selection for latent data augmentation. Neural Networks 181: 106753 (2025)- 2024
- [c6]Yuna Park, Tomoumi Takase, Keisuke Kameyama, Masaki Onishi:
Affinity-Weighted RandAugment for Problem-Oriented Augmentation. IJCNN 2024: 1-8 - [i3]Tomoumi Takase, Ryo Karakida:
Optimal Layer Selection for Latent Data Augmentation. CoRR abs/2408.13426 (2024) - 2023
- [j7]Tomoumi Takase:
Feature combination mixup: novel mixup method using feature combination for neural networks. Neural Comput. Appl. 35(17): 12763-12774 (2023) - [c5]Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa:
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. ICML 2023: 15809-15827 - 2022
- [j6]Tomoumi Takase:
A Collaborative Training Using Crowdsourcing and Neural Networks on Small and Difficult Image Classification Datasets. SN Comput. Sci. 3(2): 178 (2022) - [i2]Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa:
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. CoRR abs/2210.02720 (2022) - 2021
- [j5]Tomoumi Takase:
Dynamic batch size tuning based on stopping criterion for neural network training. Neurocomputing 429: 1-11 (2021) - [j4]Tomoumi Takase, Ryo Karakida, Hideki Asoh:
Self-paced data augmentation for training neural networks. Neurocomputing 442: 296-306 (2021) - [c4]Suguru Kanoga, Tomoumi Takase, Takayuki Hoshino, Hideki Asoh:
Time-domain Mixup Source Data Augmentation of sEMGs for Motion Recognition towards Efficient Style Transfer Mapping. EMBC 2021: 35-38 - 2020
- [i1]Tomoumi Takase, Ryo Karakida, Hideki Asoh:
Self-paced Data Augmentation for Training Neural Networks. CoRR abs/2010.15434 (2020)
2010 – 2019
- 2019
- [j3]Tomoumi Takase:
Difficulty-weighted learning: A novel curriculum-like approach based on difficult examples for neural network training. Expert Syst. Appl. 135: 83-89 (2019) - [c3]Tomoumi Takase, Satoshi Oyama, Masahito Kurihara:
Evaluation of Stratified Validation in Neural Network Training with Imbalanced Data. BigComp 2019: 1-4 - 2018
- [j2]Tomoumi Takase, Satoshi Oyama, Masahito Kurihara:
Why Does Large Batch Training Result in Poor Generalization? A Comprehensive Explanation and a Better Strategy from the Viewpoint of Stochastic Optimization. Neural Comput. 30(7) (2018) - [j1]Tomoumi Takase, Satoshi Oyama, Masahito Kurihara:
Effective neural network training with adaptive learning rate based on training loss. Neural Networks 101: 68-78 (2018) - [c2]Yukino Baba, Tomoumi Takase, Kyohei Atarashi, Satoshi Oyama, Hisashi Kashima:
Data Analysis Competition Platform for Educational Purposes: Lessons Learned and Future Challenges. AAAI 2018: 7887-7892 - [c1]Masahito Kurihara, Satoshi Oyama, Tomoumi Takase:
Longer Distance Weight Prediction for Faster Training of Neural Networks. SMC 2018: 2194-2199
Coauthor Index
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