no code implementations • 3 Dec 2023 • Viktor Zaverkin, David Holzmüller, Henrik Christiansen, Federico Errica, Francesco Alesiani, Makoto Takamoto, Mathias Niepert, Johannes Kästner
Existing biased and unbiased MD simulations, however, are prone to miss either rare events or extrapolative regions -- areas of the configurational space where unreliable predictions are made.
2 code implementations • 27 Apr 2023 • Makoto Takamoto, Francesco Alesiani, Mathias Niepert
The experiments also show several advantages of CAPE, such as its increased ability to generalize to unseen PDE parameters without large increases inference time and parameter count.
2 code implementations • 13 Oct 2022 • Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Dan MacKinlay, Francesco Alesiani, Dirk Pflüger, Mathias Niepert
With those metrics we identify tasks which are challenging for recent ML methods and propose these tasks as future challenges for the community.
no code implementations • ACL 2022 • Bhushan Kotnis, Kiril Gashteovski, Daniel Oñoro Rubio, Vanesa Rodriguez-Tembras, Ammar Shaker, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.
no code implementations • 8 Apr 2021 • Makoto Takamoto, Yusuke Morishita
In this paper, we investigated the effect of sample mixing methods, that is, Mixup, CutMix, and newly proposed Smoothed Regional Mix (SRMix), to alleviate this problem.
no code implementations • 28 Feb 2020 • Makoto Takamoto, Yusuke Morishita, Hitoshi Imaoka
In this paper, we propose a new formalism of knowledge distillation for regression problems.