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Nayantara Mudur
Nayantara Mudur
Harvard University
Verified email at g.harvard.edu
Title
Cited by
Cited by
Year
Contrastive similarity matching for supervised learning
S Qin, N Mudur, C Pehlevan
Neural computation 33 (5), 1300-1328, 2021
16*2021
Can denoising diffusion probabilistic models generate realistic astrophysical fields?
N Mudur, DP Finkbeiner
arXiv preprint arXiv:2211.12444, 2022
102022
Stellar-reddening-based Extinction Maps for Cosmological Applications
N Mudur, CF Park, DP Finkbeiner
The Astrophysical Journal 949 (2), 47, 2023
22023
Quantum Many-Body Physics Calculations with Large Language Models
H Pan, N Mudur, W Taranto, M Tikhanovskaya, S Venugopalan, Y Bahri, ...
arXiv preprint arXiv:2403.03154, 2024
12024
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
CF Park, V Ono, N Mudur, Y Ni, C Cuesta-Lazaro
arXiv preprint arXiv:2311.08558, 2023
12023
Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
V Ono, CF Park, N Mudur, Y Ni, C Cuesta-Lazaro, F Villaescusa-Navarro
arXiv preprint arXiv:2403.10648, 2024
2024
Performing Hartree-Fock many-body physics calculations with large language models
EA Kim, H Pan, N Mudur, W Taranto, S Venugopalan, Y Bahri, M Brenner
Bulletin of the American Physical Society, 2024
2024
Cosmological Field Emulation and Parameter Inference with Diffusion Models
N Mudur, C Cuesta-Lazaro, DP Finkbeiner
arXiv preprint arXiv:2312.07534, 2023
2023
A Stellar Reddening based Dust Map for Cosmological Applications
N Mudur, CF Park, C Zucker, A Saydjari, G Green, J Speagle, ...
American Astronomical Society Meeting# 240 54 (6), 143.07, 2022
2022
Towards Unifying Smooth Neural Codes with Adversarially Robust Representations
R Schaeffer, H Casademunt, N Mudur
Finite Element Analysis of Optical Waveguides
N Agarwal, N Mudur, A Sharma
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Articles 1–11