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ORCIDCarolina Cuesta-Lázaro
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2020 – today
- 2024
[i9]Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lázaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen
, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin D. Wandelt, Greg L. Bryan:
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology. CoRR abs/2402.05137 (2024)- [i8]Nayantara Mudur, Carolina Cuesta-Lázaro, Douglas P. Finkbeiner:
Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo. CoRR abs/2405.05255 (2024) - [i7]Tri Nguyen, Francisco Villaescusa-Navarro, Siddharth Mishra-Sharma, Carolina Cuesta-Lázaro, Paul Torrey, Arya Farahi, Alex M. Garcia, Jonah C. Rose, Stephanie O'Neil, Mark Vogelsberger, Xuejian Shen, Cian Roche, Daniel Anglés-Alcázar, Nitya Kallivayalil, Julian B. Muñoz, Francis-Yan Cyr-Racine, Sandip Roy, Lina Necib, Kassidy E. Kollmann:
How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds. CoRR abs/2409.02980 (2024) - [i6]Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lázaro, Tommi S. Jaakkola, Tess E. Smidt:
A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. CoRR abs/2410.20516 (2024) - 2023
- [c3]Arnau Quera-Bofarull, Ayush Chopra, Joseph Aylett-Bullock, Carolina Cuesta-Lázaro, Anisoara Calinescu, Ramesh Raskar, Michael J. Wooldridge:
Don't Simulate Twice: One-Shot Sensitivity Analyses via Automatic Differentiation. AAMAS 2023: 1867-1876 - [i5]Mayeul Aubin, Carolina Cuesta-Lázaro, Ethan Tregidga, Javier Viaña, Cecilia Garraffo, Iouli E. Gordon, Mercedes López-Morales, Robert J. Hargreaves, Vladimir Yu. Makhnev, Jeremy J. Drake, Douglas P. Finkbeiner, Phillip Cargile:
Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows. CoRR abs/2309.09337 (2023) - [i4]Core Francisco Park, Victoria Ono, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lázaro:
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models. CoRR abs/2311.08558 (2023) - [i3]Carolina Cuesta-Lázaro, Siddharth Mishra-Sharma:
A point cloud approach to generative modeling for galaxy surveys at the field level. CoRR abs/2311.17141 (2023) - [i2]Nayantara Mudur, Carolina Cuesta-Lázaro, Douglas P. Finkbeiner:
Cosmological Field Emulation and Parameter Inference with Diffusion Models. CoRR abs/2312.07534 (2023) - 2022
- [c2]Carolina Cuesta-Lázaro, Animesh Prasad, Trevor Wood:
What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels. ACL (1) 2022: 5820-5829 - 2021
- [j1]Joseph Aylett-Bullock, Carolina Cuesta-Lázaro, Arnau Quera-Bofarull, Anjali Katta, Katherine Hoffmann Pham, Benjamin Hoover, Hendrik Strobelt, Rebeca Moreno Jimenez, Aidan Sedgewick, Egmond Samir Evers, David Kennedy, Sandra Harlass, Allen Gidraf Kahindo Maina, Ahmad Hussien, Miguel A. Luengo-Oroz:
Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox's Bazar settlement. PLoS Comput. Biol. 17(10) (2021)
2010 – 2019
- 2019
- [c1]Joseph Bullock, Carolina Cuesta-Lázaro, Arnau Quera-Bofarull:
XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets. Biomedical Applications in Molecular, Structural, and Functional Imaging 2019: 109531Z - 2018
- [i1]Joseph Bullock, Carolina Cuesta-Lázaro, Arnau Quera-Bofarull:
XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets. CoRR abs/1812.00548 (2018)
Coauthor Index
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