4 papers
Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks
Yuto Omae, Kazuki Sakai, Yohei Kakimoto +3
The flatness hypothesis suggests that flatness of the loss landscape, as measured by the eigenvalues of the loss Hessian, correlates with better neural network generalization. Whil…
Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks
Yuto Omae, Kazuki Sakai, Yohei Kakimoto +3
Neural networks (NNs) are central to modern machine learning and achieve state-of-the-art results in many applications. However, the relationship between loss geometry and generali…
Computing the Wave: Where the Gravitational Wave Community benefits from High-Energy Physics, and where it differs ?
Marco Meyer-Conde, Nobuyuki Kanda, Hirotaka Takahashi +2
High-Energy Physics (HEP) and Gravitational Wave (GW) communities serve different scientific purposes. However, their methodologies might potentially offer mutual enrichment throug…
Parameter estimation of protoneutron stars from gravitational wave signals using the Hilbert-Huang transform
Seiya Sasaoka, Yusuke Sakai, Diego Dominguez +5
Core-collapse supernovae (CCSNe) are potential multimessenger events detectable by current and future gravitational wave (GW) detectors. The GW signals emitted during these events…