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ORCIDHiroki Naganuma
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2020 – today
- 2024
[j2]Kotaro Yoshida, Hiroki Naganuma:
Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration. Trans. Mach. Learn. Res. 2024 (2024)- [c7]Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas:
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. ICML 2024 - [i8]Kotaro Yoshida, Hiroki Naganuma:
Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration. CoRR abs/2401.17541 (2024) - [i7]Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda, Takafumi Horie, Hiroki Naganuma:
Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation. CoRR abs/2404.01334 (2024) - [i6]Masanari Kimura, Hiroki Naganuma:
Geometric Insights into Focal Loss: Reducing Curvature for Enhanced Model Calibration. CoRR abs/2405.00442 (2024) - 2023
- [j1]Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas:
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. Trans. Mach. Learn. Res. 2023 (2023) - [c6]Hiroki Naganuma, Hideaki Iiduka:
Conjugate Gradient Method for Generative Adversarial Networks. AISTATS 2023: 4381-4408 - [c5]Keigo Tada, Hiroki Naganuma:
How Image Corruption and Perturbation Affect Out-of-Distribution Generalization and Calibration. IJCNN 2023: 1-6 - [c4]Haruka Kumagai
, Ryosuke Yamaki, Hiroki Naganuma:
Story-to-Images Translation: Leveraging Diffusion Models and Large Language Models for Sequence Image Generation. NarSUM@MM 2023: 57-63 - [i5]Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas:
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths. CoRR abs/2306.11922 (2023) - [i4]Hiroki Naganuma, Ryuichiro Hataya:
An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration. CoRR abs/2307.08187 (2023) - 2022
- [c3]Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas:
Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation. CoLLAs 2022: 966-981 - [i3]Hiroki Naganuma, Hideaki Iiduka:
Conjugate Gradient Method for Generative Adversarial Networks. CoRR abs/2203.14495 (2022) - [i2]Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas:
Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation. CoRR abs/2206.11180 (2022) - [i1]Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas:
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. CoRR abs/2211.08583 (2022)
2010 – 2019
- 2019
- [c2]Hiroki Naganuma, Rio Yokota:
A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training. CCGRID 2019: 696-703 - 2017
- [c1]Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota:
Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. HPCS 2017: 186-192
Coauthor Index
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