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ORCIDMakoto Takamoto
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2020 – today
- 2024
[j3]Bernd Flemisch, Sibylle Hermann
, Melanie Herschel, Dirk Pflüger, Jürgen Pleiss, Jan Range, Sarbani Roy, Makoto Takamoto, Benjamin Uekermann:
Research Data Management in Simulation Science: Infrastructure, Tools, and Applications. Datenbank-Spektrum 24(2): 97-105 (2024)- [i8]Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert:
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. CoRR abs/2405.14253 (2024) - [i7]Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert:
Active Learning for Neural PDE Solvers. CoRR abs/2408.01536 (2024) - [i6]Makoto Takamoto, Viktor Zaverkin, Mathias Niepert:
Physics-Informed Weakly Supervised Learning for Interatomic Potentials. CoRR abs/2408.05215 (2024) - 2023
- [c5]Makoto Takamoto, Francesco Alesiani, Mathias Niepert:
Learning Neural PDE Solvers with Parameter-Guided Channel Attention. ICML 2023: 33448-33467 - [i5]Makoto Takamoto, Francesco Alesiani, Mathias Niepert:
Learning Neural PDE Solvers with Parameter-Guided Channel Attention. CoRR abs/2304.14118 (2023) - 2022
- [c4]Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence:
MILIE: Modular & Iterative Multilingual Open Information Extraction. ACL (1) 2022: 6939-6950 - [c3]Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert:
PDEBench: An Extensive Benchmark for Scientific Machine Learning. NeurIPS 2022 - [i4]Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert:
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning. CoRR abs/2210.07182 (2022) - 2021
- [c2]Makoto Takamoto, Yusuke Morishita:
An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks. MIPR 2021: 49-55 - [i3]Makoto Takamoto, Yusuke Morishita:
An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks. CoRR abs/2104.03535 (2021) - [i2]Bhushan Kotnis, Kiril Gashteovski, Carolin Lawrence, Daniel Oñoro-Rubio, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert:
Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction. CoRR abs/2110.08144 (2021) - 2020
- [c1]Makoto Takamoto, Yusuke Morishita, Hitoshi Imaoka:
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation. MIPR 2020: 67-72 - [i1]Makoto Takamoto, Yusuke Morishita, Hitoshi Imaoka:
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation. CoRR abs/2002.12597 (2020)
2010 – 2019
- 2014
- [j2]Yukinao Akamatsu, Shu-ichiro Inutsuka, Chiho Nonaka, Makoto Takamoto:
A new scheme of causal viscous hydrodynamics for relativistic heavy-ion collisions: A Riemann solver for quark-gluon plasma. J. Comput. Phys. 256: 34-54 (2014) - 2011
- [j1]Makoto Takamoto, Shu-ichiro Inutsuka:
A fast numerical scheme for causal relativistic hydrodynamics with dissipation. J. Comput. Phys. 230(18): 7002-7017 (2011)
Coauthor Index
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