3 papers
cs.LG2026
Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation
Genki Osada
While diffusion models enable new approaches for estimating Local Intrinsic Dimension (LID), existing methods fail in high-dimensional spaces where noise from vast normal direction…
cs.LG2024
Local Curvature Smoothing with Stein's Identity for Efficient Score Matching
Genki Osada, Makoto Shing, Takashi Nishide
The training of score-based diffusion models (SDMs) is based on score matching. The challenge of score matching is that it includes a computationally expensive Jacobian trace. Whil…
cs.LG2024
Understanding Likelihood of Normalizing Flow and Image Complexity through the Lens of Out-of-Distribution Detection
Genki Osada, Tsubasa Takahashi, Takashi Nishide
Out-of-distribution (OOD) detection is crucial to safety-critical machine learning applications and has been extensively studied. While recent studies have predominantly focused on…