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cs.LG2026
Unified generalization analysis for physics informed neural networks
Yuka Hashimoto, Tomoharu Iwata
Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them useful for scientific problems.…
cs.LG2026
Relative Density Ratio Optimization for Stable and Statistically Consistent Model Alignment
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +4
Aligning language models with human preferences is essential for ensuring their safety and reliability. Although most existing approaches assume specific human preference models su…
cs.LG2024
Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices
Yuka Hashimoto, Tomoharu Iwata
We propose deep Koopman-layered models with learnable parameters in the form of Toeplitz matrices for analyzing the transition of the dynamics of time-series data. The proposed mod…