Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction. - Takamoto, Makoto; Oñoro-Rubio, Daniel; Rim, Wiem Ben; Maruyama, Takashi; Kotnis, Bhushan (Trans. Mach. Learn. Res., 2025)

Active Learning for Neural PDE Solvers. - Musekamp, Daniel; Kalimuthu, Marimuthu; Holzmüller, David; Takamoto, Makoto; Niepert, Mathias (2025)

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations. - Christiansen, Henrik; Maruyama, Takashi; Errica, Federico; Zaverkin, Viktor; Takamoto, Makoto; Alesiani, Francesco (CoRR, 2025)

Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction. - Takamoto, Makoto; Oñoro-Rubio, Daniel; Rim, Wiem Ben; Maruyama, Takashi; Kotnis, Bhushan (CoRR, 2025)

Research Data Management in Simulation Science: Infrastructure, Tools, and Applications. - Flemisch, Bernd; Hermann, Sibylle; Herschel, Melanie; Pflüger, Dirk; Pleiss, Jürgen; Range, Jan; Roy, Sarbani; Takamoto, Makoto; Uekermann, Benjamin (Datenbank-Spektrum, 2024)

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. - Zaverkin, Viktor; Alesiani, Francesco; Maruyama, Takashi; Errica, Federico; Christiansen, Henrik; Takamoto, Makoto; Weber, Nicolas; Niepert, Mathias (2024)

PDEBench Datasets. (8.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2024)

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. - Zaverkin, Viktor; Alesiani, Francesco; Maruyama, Takashi; Errica, Federico; Christiansen, Henrik; Takamoto, Makoto; Weber, Nicolas; Niepert, Mathias (CoRR, 2024)

Active Learning for Neural PDE Solvers. - Musekamp, Daniel; Kalimuthu, Marimuthu; Holzmüller, David; Takamoto, Makoto; Niepert, Mathias (CoRR, 2024)

Physics-Informed Weakly Supervised Learning for Interatomic Potentials. - Takamoto, Makoto; Zaverkin, Viktor; Niepert, Mathias (CoRR, 2024)

Learning Neural PDE Solvers with Parameter-Guided Channel Attention. - Takamoto, Makoto; Alesiani, Francesco; Niepert, Mathias (2023)

PDEBench Pretrained Models. (2.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2023)

PDEBench Datasets. (3.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2023)

PDEBench Datasets. (4.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2023)

PDEBench Datasets. (5.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2023)

PDEBench Datasets. (6.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2023)

PDEBench Datasets. (7.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2023)

Learning Neural PDE Solvers with Parameter-Guided Channel Attention. - Takamoto, Makoto; Alesiani, Francesco; Niepert, Mathias (CoRR, 2023)

MILIE: Modular & Iterative Multilingual Open Information Extraction. - Kotnis, Bhushan; Gashteovski, Kiril; Oñoro-Rubio, Daniel; Shaker, Ammar; Rodriguez-Tembras, Vanesa; Takamoto, Makoto; Niepert, Mathias; Lawrence, Carolin (2022)

PDEBench: An Extensive Benchmark for Scientific Machine Learning. - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (2022)

PDEBench Pretrained Models. (1.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2022)

PDEBench Datasets. (1.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2022)

PDEBench Datasets. (2.0) - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (DaRUS, 2022)

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning. - Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Daniel; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias (CoRR, 2022)

An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks. - Takamoto, Makoto; Morishita, Yusuke (2021)

An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks. - Takamoto, Makoto; Morishita, Yusuke (CoRR, 2021)

Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction. - Kotnis, Bhushan; Gashteovski, Kiril; Lawrence, Carolin; Oñoro-Rubio, Daniel; Rodriguez-Tembras, Vanesa; Takamoto, Makoto; Niepert, Mathias (CoRR, 2021)

An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation. - Takamoto, Makoto; Morishita, Yusuke; Imaoka, Hitoshi (2020)

An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation. - Takamoto, Makoto; Morishita, Yusuke; Imaoka, Hitoshi (CoRR, 2020)

A new scheme of causal viscous hydrodynamics for relativistic heavy-ion collisions: A Riemann solver for quark-gluon plasma. - Akamatsu, Yukinao; Inutsuka, Shu-ichiro; Nonaka, Chiho; Takamoto, Makoto (J. Comput. Phys., 2014)

A fast numerical scheme for causal relativistic hydrodynamics with dissipation. - Takamoto, Makoto; Inutsuka, Shu-ichiro (J. Comput. Phys., 2011)