50 citations · 314 across the 30 of their papers we have counts for
32 papers
Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection
Hyungjoon Soh, Dongha Lee, Vipul Periwal +1
Selecting key variables from high-dimensional data is increasingly important in the era of big data. Sparse regression serves as a powerful tool for this purpose by promoting model…
Multiplicative learning from observation-prediction ratios
Han Kim, Hyungjoon Soh, Vipul Periwal +1
Additive parameter updates, as used in gradient descent and its adaptive extensions, underpin most modern machine-learning optimization. Yet, such additive schemes often demand num…
Annealing approach to root-finding
Junghyo Jo, Alexandre Wagemakers, Vipul Periwal
The Newton-Raphson method is a fundamental root-finding technique with numerous applications in physics. In this study, we propose a parameterized variant of the Newton-Raphson met…
Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry
Yong-Hyun Park, Mingi Kwon, Jaewoong Choi +2
Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space. To understand the latent space , we analy…
Tradeoff of generalization error in unsupervised learning
Gilhan Kim, Hojun Lee, Junghyo Jo +1
Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically…
Unsupervised Discovery of Semantic Latent Directions in Diffusion Models
Yong-Hyun Park, Mingi Kwon, Junghyo Jo +1
Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space. While image editing with GANs builds upon latent space, DMs rely on edi…