1 citations · 1 across the 7 of their papers we have counts for
7 papers
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…
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…
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
Maohao Shen, J. Jon Ryu, Soumya Ghosh +4
This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called \emph{evidential deep learning} (EDL), in which a single neural network mo…
Gambling-Based Confidence Sequences for Bounded Random Vectors
J. Jon Ryu, Gregory W. Wornell
A confidence sequence (CS) is a sequence of confidence sets that contains a target parameter of an underlying stochastic process at any time step with high probability. This paper…
Operator SVD with Neural Networks via Nested Low-Rank Approximation
J. Jon Ryu, Xiangxiang Xu, H. S. Melihcan Erol +3
Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scien…