1 citations · 1 across the 4 of their papers we have counts for
6 papers
MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching
Yesom Park, Kelvin Kan, Qifan Chen +4
Flow matching models excel at generative modeling, and many downstream applications require their samples to satisfy prescribed constraints, such as observed measurements and physi…
Compressed Active Subspaces for Scalable Bayesian Inference
Thomas Flynn, Sanket Jantre, Byung-Jun Yoon +1
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions tha…
Dynamical Implicit Neural Representations
Yesom Park, Kelvin Kan, Thomas Flynn +4
Implicit Neural Representations (INRs) provide a powerful continuous framework for modeling complex visual and geometric signals, but spectral bias remains a fundamental challenge,…
Problem-dependent convergence bounds for randomized linear gradient compression
Thomas Flynn, Patrick Johnstone, Shinjae Yoo
In distributed optimization, the communication of model updates can be a performance bottleneck. Consequently, gradient compression has been proposed as a means of increasing optim…
An Evaluation of Real-time Adaptive Sampling Change Point Detection Algorithm using KCUSUM
Vijayalakshmi Saravanan, Perry Siehien, Shinjae Yoo +4
Detecting abrupt changes in real-time data streams from scientific simulations presents a challenging task, demanding the deployment of accurate and efficient algorithms. Identifyi…
SuperVortexNet: Reconstructing Superfluid Vortex Filaments Using Deep Learning
Nick Keepfer, Thomas Flynn, Nick Parker +1
We introduce a novel approach to the three-dimensional reconstruction of superfluid vortex filaments using deep convolutional neural networks. Superfluid vortices, quantum mechanic…