| Accelerating matrix multiplication in deep learning by using low-rank approximation K Osawa, A Sekiya, H Naganuma, R Yokota 2017 International Conference on High Performance Computing & Simulation …, 2017 | 21 | 2017 |
| Empirical Study on Optimizer Selection for Out-of-Distribution Generalization Hiroki Naganuma, Kartik Ahuj, Shiro Takag, Tetsuya Motokawa, Rio Yokota ... Transactions on Machine Learning Research, 2023 | 8* | 2023 |
| Optimal transport meets noisy label robust loss and mixup regularization for domain adaptation K Fatras, H Naganuma, I Mitliagkas Conference on Lifelong Learning Agents, 966-981, 2022 | 5 | 2022 |
| A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training H Naganuma, R Yokota CCGRID 2019, 696-703, 2019 | 5 | 2019 |
| Accelerating Convolutional Neural Networks Using Low Precision Arithmetic H Naganuma, R Yokota HPC Asia 2018, 2018 | 5 | 2018 |
| 低ランク近似を用いた深層学習の行列積の高速化 関谷翠， 大沢和樹， 長沼大樹， 横田理央 研究報告ハイパフォーマンスコンピューティング (HPC) 2017 (24), 1-7, 2017 | 3 | 2017 |
| An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration H Naganuma, R Hataya, I Mitliagkas arXiv preprint arXiv:2307.08187, 2023 | 1 | 2023 |
| Necessary and Sufficient Hypothesis of Curvature: Understanding Connection Between Out-of-Distribution Generalization and Calibration H Naganuma, M Kimura ICLR2023 Workshop on Domain Generalization, 2023 | 1 | 2023 |
| Conjugate gradient method for generative adversarial networks H Naganuma, H Iiduka International Conference on Artificial Intelligence and Statistics, 4381-4408, 2023 | 1 | 2023 |
| Design of a Smart Pacifier to Detect Dehydration in Babies and Shaping Parents' Behavior M SUGIMOTO, E YAMATSUTA, H NAGANUMA, R ARAKAWA, Y USHIRO, ... TSUKUBA GLOBAL SCIENCE WEEK 2018 ART & DESIGN SESSION PROCEEDINGS, 35-38, 2018 | 1 | 2018 |
| Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation Y Naraki, R Yamaki, Y Ikeda, T Horie, H Naganuma arXiv preprint arXiv:2404.01334, 2024 | | 2024 |
| Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration K Yoshida, H Naganuma arXiv preprint arXiv:2401.17541, 2024 | | 2024 |
| A Survey on Product Placement Strategies: Evaluating Effectiveness and Persuasion Resistance L Fujima, Y Mamiya, H Naganuma Available at SSRN 4649030, 2023 | | 2023 |
| Story-to-Images Translation: Leveraging Diffusion Models and Large Language Models for Sequence Image Generation H Kumagai, R Yamaki, H Naganuma Proceedings of the 2nd Workshop on User-centric Narrative Summarization of …, 2023 | | 2023 |
| No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths C Guille-Escuret, H Naganuma, K Fatras, I Mitliagkas arXiv preprint arXiv:2306.11922, 2023 | | 2023 |
| On the Interplay of Curvature, Calibration and Out-of-Distribution Generalization: Insights from SAM and Focal Loss Analyses H Naganuma, M Kimura | | 2023 |
| How Image Corruption and Perturbation Affect Out-Of-Distribution Generalization and Calibration K Tada, H Naganuma International Joint Conference on Neural Networks (IJCNN 2023), 2023 | | 2023 |
| Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime H Naganuma, T Suzuki, R Yokota, M Nomura, K Ishikawa, I Sato | | 2021 |
| Astral Body: 強磁性粉末を用いた動的なディスプレイによる生き物らしさの表現 山本景子， 長沼大樹， 油井俊哉， 原田誠史， 福谷和芳， 堀修生， 福澤貴之 日本バーチャルリアリティ学会論文誌 24 (3), 313-323, 2019 | | 2019 |
| Accelerating Convolutional Neural Networks Using Low-Rank Tensor Decomposition K Osawa, A Sekiya, H Naganuma, R Yokota IEICE Technical Report; IEICE Tech. Rep. 117 (238), 1-6, 2017 | | 2017 |
Rio YokotaProfessor, Tokyo Institute of Technology確認したメール アドレス: gsic.titech.ac.jp
