219 citations · 330 across the 11 of their papers we have counts for
15 papers
An exploration of plug-and-play flow matching for full-waveform inversion on breast imaging
Cristina Garcia-Cardona, Siting Liu, Fatma Terzioglu +2
Full-waveform inversion (FWI) reconstructs sound-speed fields from measured waveforms, by solving an optimization problem that minimizes the discrepancy between measured waveforms…
Plug-and-Play Image Restoration with Flow Matching: A Continuous Viewpoint
Fan Jia, Yuhao Huang, Shih-Hsin Wang +3
Flow matching-based generative models have been integrated into the plug-and-play image restoration framework, and the resulting plug-and-play flow matching (PnP-Flow) model has ac…
Deep Parameter Interpolation for Scalar Conditioning
Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona +2
We propose deep parameter interpolation (DPI), a general-purpose method for transforming an existing deep neural network architecture into one that accepts an additional scalar inp…
A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundles
Gokul Bhusal, Yifei Lou, Cristina Garcia-Cardona +1
Due to low spatial resolution, hyperspectral data often consists of mixtures of contributions from multiple materials. This limitation motivates the task of hyperspectral unmixing…
Plug-and-Play Priors as a Score-Based Method
Chicago Y. Park, Yuyang Hu, Michael T. McCann +3
Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-ba…
Random Walks with Tweedie: A Unified View of Score-Based Diffusion Models
Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona +2
We present a concise derivation for several influential score-based diffusion models that relies on only a few textbook results. Diffusion models have recently emerged as powerful…
A cross-study analysis of drug response prediction in cancer cell lines
Fangfang Xia, Jonathan Allen, Prasanna Balaprakash +21
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm deve…
Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture
Cristina Garcia-Cardona, M. Giselle Fernández-Godino, Daniel O'Malley +1
Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive. The cost increases even more if multiple…
Convolutional Dictionary Learning: A Comparative Review and New Algorithms
Cristina Garcia-Cardona, Brendt Wohlberg
Convolutional sparse representations are a form of sparse representation with a dictionary that has a structure that is equivalent to convolution with a set of linear filters. Whil…
First and Second Order Methods for Online Convolutional Dictionary Learning
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg +1
Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to d…
Online Convolutional Dictionary Learning
Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg +1
While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem. The single biggest impediment to learni…
Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization
Cristina Garcia-Cardona, Arjuna Flenner, Allon G. Percus
We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivat…
Spectral Clustering with Epidemic Diffusion
Laura M. Smith, Kristina Lerman, Cristina Garcia-Cardona +2
Spectral clustering is widely used to partition graphs into distinct modules or communities. Existing methods for spectral clustering use the eigenvalues and eigenvectors of the gr…
Multiclass Data Segmentation using Diffuse Interface Methods on Graphs
Cristina Garcia-Cardona, Ekaterina Merkurjev, Andrea L. Bertozzi +2
We present two graph-based algorithms for multiclass segmentation of high-dimensional data. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, re…
Multiclass Diffuse Interface Models for Semi-Supervised Learning on Graphs
Cristina Garcia-Cardona, Arjuna Flenner, Allon G. Percus
We present a graph-based variational algorithm for multiclass classification of high-dimensional data, motivated by total variation techniques. The energy functional is based on a…