2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
Contrastive Predictive Coding Done Right for Mutual Information Estimation
J. Jon Ryu, Pavan Yeddanapudi, Xiangxiang Xu +1
The InfoNCE objective, originally introduced for contrastive representation learning, has become a popular choice for mutual information (MI) estimation, despite its indirect conne…
Revisiting Orbital Minimization Method for Neural Operator Decomposition
J. Jon Ryu, Samuel Zhou, Gregory W. Wornell
Spectral decomposition of linear operators plays a central role in many areas of machine learning and scientific computing. Recent work has explored training neural networks to app…
Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption
Yepeng Liu, Xuandong Zhao, Dawn Song +2
Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap stems from misaligned incentives among L…
Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
Minchan Jeong, J. Jon Ryu, Se-Young Yun +1
The Koopman operator provides a principled framework for analyzing nonlinear dynamical systems through linear operator theory. Recent advances in dynamic mode decomposition (DMD) h…
Score-of-Mixture Training: Training One-Step Generative Models Made Simple via Score Estimation of Mixture Distributions
Tejas Jayashankar, J. Jon Ryu, Gregory Wornell
We propose Score-of-Mixture Training (SMT), a novel framework for training one-step generative models by minimizing a class of divergences called the -skew Jensen--Shannon diver…