4 citations · 4 across the 3 of their papers we have counts for
8 papers
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning
Zhen Lin, Nicholas Huang, Camille Avestruz +4
Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of…
Machine learning of high dimensional data on a noisy quantum processor
Evan Peters, João Caldeira, Alan Ho +6
We present a quantum kernel method for high-dimensional data analysis using Google's universal quantum processor, Sycamore. This method is successfully applied to the cosmological…
Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms
João Caldeira, Brian Nord
We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty…
Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan"
J. Amundson, J. Annis, C. Avestruz +27
We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development…
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
Brian Nord, Andrew J. Connolly, Jamie Kinney +35
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical mode…
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer
João Caldeira, Joshua Job, Steven H. Adachi +2
We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems. Morphological…