Tootfinder

Dimension-Decomposed Learning for Quadrotor Geometric Attitude Control with Almost Global Exponential Convergence on SO(3)
Tianhua Gao, Masashi Izumita, Kohji Tomita, Akiya Kamimura
https://arxiv.org/abs/2508.14422

Dimension-Decomposed Learning for Quadrotor Geometric Attitude Control with Almost Global Exponential Convergence on SO(3)
This paper introduces a lightweight and interpretable online learning approach called Dimension-Decomposed Learning (DiD-L) for disturbance identification in quadrotor geometric attitude control. As a module instance of DiD-L, we propose the Sliced Adaptive-Neuro Mapping (SANM). Specifically, to address underlying underfitting problems, the high-dimensional mapping for online identification is axially ``sliced" into multiple low-dimensional submappings (slices). In this way, the complex high-di…

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Harrison Katz, Liz Medina, Robert E. Weiss
https://arxiv.org/abs/2506.13973

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Prior choice can strongly influence Bayesian Dirichlet ARMA (B-DARMA) inference for compositional time-series. Using simulations with (i) correct lag order, (ii) overfitting, and (iii) underfitting, we assess five priors: weakly-informative, horseshoe, Laplace, mixture-of-normals, and hierarchical. With the true lag order, all priors achieve comparable RMSE, though horseshoe and hierarchical slightly reduce bias. Under overfitting, aggressive shrinkage-especially the horseshoe-suppresses noise …

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis
Yao Ni, Song Wen, Piotr Koniusz, Anoop Cherian
https://arxiv.org/abs/2506.06483

Noise Consistency Regularization for Improved Subject-Driven Image Synthesis
Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, we propose two auxiliary consistency losses for diffusion fine-tuning. First, a prior consistency regul…