1 citations · 1 across the 8 of their papers we have counts for
8 papers
Should the Boundary Term Be Learned in Reflected Diffusion? Conormal Trace and Reflection Masking
Ziyue Wang, Takafumi Kanamori
We study score learning for reflected diffusion on bounded domains. Reflection keeps trajectories feasible but does not ensure that the learned score satisfies the boundary behavio…
Fast boundary-aware spatial intensity estimation on complex domains
Takumi Nakagawa, Kōsaku Takanashi, Kenichiro McAlinn +1
Spatial intensity maps are routinely used to summarize point patterns on geographically constrained regions, such as islands, coastlines, watersheds, ecological reserves, and admin…
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
Yushi Hirose, Hiroo Irobe, Takafumi Kanamori
Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated. While PNU learning, a risk rewriting meth…
Distributional Spectral Diagnostics for Localizing Grokking Transitions
Ziyue Wang, Yufeng Ying, Takafumi Kanamori
In grokking, a model first fits the training data while test accuracy remains low, and only later begins to generalize. We ask whether this transition can be localized from observe…
TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation
Yuhui Zhang, Dongshen Wu, Yuichiro Wada +1
A reliable uncertainty estimation method is the foundation of many modern out-of-distribution (OOD) detectors, which are critical for safe deployments of deep learning models in th…
Statistical learnability of smooth boundaries via pairwise binary classification with deep ReLU networks
Hiroki Waida, Takafumi Kanamori
The topic of nonparametric estimation of smooth boundaries is extensively studied in the conventional setting where pairs of single covariate and response variable are observed. Ho…