Makoto Takamoto

According to our database, Makoto Takamoto authored at least 26 papers between 2011 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists.

Seungone Kim, Dongkeun Yoon, Kiril Gashteovski, Juyoung Suk, Jinheon Baek, Pranjal Aggarwal, Ian Wu, Viktor Zaverkin, Spase Petkoski, Daniel R. Schrider, Ilija Dukovski, Francesco Santini, Biljana Mitreska, Yong Jeong, Kyeongha Kwon, Young Min Sim, Dragana Manasova, Arthur Porto, Biljana Mojsoska, Makoto Takamoto, Marko Shuntov, Ruoqi Liu, Hyunjoo Jenny Lee, Niyazi Ulas Dinç, Yehhyun Jo, Sunkyu Han, Chungwoo Lee, Huishan Li, Esther H. R. Tsai, Ergun Simsek, Khushboo Shafi, Yeonseung Chung, Jihye Park, Aleksandar Shulevski, Henrik Christiansen, Yoosang Son, Elly Knight, Amanda Montoya, Jeongyoun Ahn, Christian Langkammer, Heera Moon, Changwon Yoon, Nikola Stikov, Mooseok Jang, Edward Choi, Junhan Kim, Yeon Sik Jung, Woo Youn Kim, Jae Kyoung Kim, Ishraq Md Anjum, Hyun Uk Kim, Drew Bridges, Carolin Lawrence, Xiang Yue, Alice Oh, Akari Asai, Sean Welleck, Graham Neubig

CoRR, May, 2026

2025

Transferable long-range interactions in machine-learned interatomic potentials: Dataset.

Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Henrik Christiansen, Makoto Takamoto, Federico Errica, Mathias Niepert

Dataset, December, 2025

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations.

Henrik Christiansen, Takashi Maruyama, Federico Errica, Viktor Zaverkin, Makoto Takamoto, Francesco Alesiani

CoRR, March, 2025

Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction.

Makoto Takamoto, Daniel Oñoro-Rubio, Wiem Ben Rim, Takashi Maruyama, Bhushan Kotnis

Trans. Mach. Learn. Res., 2025

Physics-Informed Weakly Supervised Learning For Interatomic Potentials.

Makoto Takamoto, Viktor Zaverkin, Mathias Niepert

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Active Learning for Neural PDE Solvers.

Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller, Makoto Takamoto, Mathias Niepert

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Research Data Management in Simulation Science: Infrastructure, Tools, and Applications.

Bernd Flemisch, Sibylle Hermann, Melanie Herschel, Dirk Pflüger, Jürgen Pleiss, Jan Range, Sarbani Roy, Makoto Takamoto, Benjamin Uekermann

Datenbank-Spektrum, July, 2024

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, February, 2024

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing.

Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, November, 2023

PDEBench Pretrained Models.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, November, 2023

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, October, 2023

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, September, 2023

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, August, 2023

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, April, 2023

Learning Neural PDE Solvers with Parameter-Guided Channel Attention.

Makoto Takamoto, Francesco Alesiani, Mathias Niepert

Proceedings of the International Conference on Machine Learning, 2023

2022

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, October, 2022

PDEBench Datasets.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, July, 2022

PDEBench Pretrained Models.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Dataset, June, 2022

PDEBench: An Extensive Benchmark for Scientific Machine Learning.

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MILIE: Modular & Iterative Multilingual Open Information Extraction.

Bhushan Kotnis, Kiril Gashteovski, Daniel Oñoro-Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction.

Bhushan Kotnis, Kiril Gashteovski, Carolin Lawrence, Daniel Oñoro-Rubio, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert

CoRR, 2021

An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks.

Makoto Takamoto, Yusuke Morishita

Proceedings of the 4th IEEE International Conference on Multimedia Information Processing and Retrieval, 2021

2020

An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation.

Makoto Takamoto, Yusuke Morishita, Hitoshi Imaoka

Proceedings of the 3rd IEEE Conference on Multimedia Information Processing and Retrieval, 2020

2014

A new scheme of causal viscous hydrodynamics for relativistic heavy-ion collisions: A Riemann solver for quark-gluon plasma.

Yukinao Akamatsu, Shu-ichiro Inutsuka, Chiho Nonaka, Makoto Takamoto

J. Comput. Phys., 2014

2011

A fast numerical scheme for causal relativistic hydrodynamics with dissipation.

Makoto Takamoto, Shu-ichiro Inutsuka

J. Comput. Phys., 2011