activity
20182022
most citedSampling for Deep Learning Model Diagnosis (Technical Report)

2 citations · 5 across the 3 of their papers we have counts for

collaborators

9 papers

astro-ph.IM20222 cited

SARABANDE: 3/4 Point Correlation Functions with Fast Fourier Transforms

James Sunseri, Zachary Slepian, Stephen Portillo +3

We present a new package SARABANDE for measuring 3 & 4 Point Correlation Functions (3/4 PCFs) in time using Fast Fourier T…

astro-ph.GA2020

Classification of Magnetohydrodynamic Simulations using Wavelet Scattering Transforms

Andrew K. Saydjari, Stephen K. N. Portillo, Zachary Slepian +3

The complex interplay of magnetohydrodynamics, gravity, and supersonic turbulence in the interstellar medium (ISM) introduces non-Gaussian structure that can complicate comparison…

astro-ph.GA2020

The Catalogue for Astrophysical Turbulence Simulations (CATS)

B. Burkhart, S. Appel, S. Bialy +18

Turbulence is a key process in many fields of astrophysics. Advances in numerical simulations of fluids over the last several decades have revolutionized our understanding of turbu…

cs.LG20202 cited

Sampling for Deep Learning Model Diagnosis (Technical Report)

Parmita Mehta, Stephen Portillo, Magdalena Balazinska +1

Deep learning (DL) models have achieved paradigm-changing performance in many fields with high dimensional data, such as images, audio, and text. However, the black-box nature of d…

astro-ph.IM2020

Dimensionality Reduction of SDSS Spectra with Variational Autoencoders

Stephen K. N. Portillo, John K. Parejko, Jorge R. Vergara +1

High resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information the…

astro-ph.IM2019

Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point Source Detection and Deblending

Richard M. Feder, Stephen K. N. Portillo, Tansu Daylan +1

Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields (Portillo et al. 2017). We extend our work to multiple band…