509 citations · 3.2k across the 61 of their papers we have counts for
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DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder +6
With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally…
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
A. Ćiprijanović, D. Kafkes, G. N. Perdue +6
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these…
Importance of Kernel Bandwidth in Quantum Machine Learning
Ruslan Shaydulin, Stefan M. Wild
Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of…
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations
Stephen Hudson, Jeffrey Larson, John-Luke Navarro +1
Almost all applications stop scaling at some point; those that don't are seldom performant when considering time to solution on anything but aspirational/unicorn resources. Recogni…
Randomized Algorithms for Scientific Computing (RASC)
Aydin Buluc, Tamara G. Kolda, Stefan M. Wild +16
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office o…
Scalable Statistical Inference of Photometric Redshift via Data Subsampling
Arindam Fadikar, Stefan M. Wild, Jonas Chaves-Montero
Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models…