Brian Nord

Orcid: 0000-0001-6706-8972

According to our database1, Brian Nord authored at least 35 papers between 2016 and 2024.

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Bibliography

2024
Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks.
CoRR, 2024

2023
A robust estimator of mutual information for deep learning interpretability.
Mach. Learn. Sci. Technol., June, 2023

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection.
Mach. Learn. Sci. Technol., June, 2023

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning.
CoRR, 2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets.
CoRR, 2023

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers.
CoRR, 2023

WavPool: A New Block for Deep Neural Networks.
CoRR, 2023

2022
DIGS: deep inference of galaxy spectra with neural posterior estimation.
Mach. Learn. Sci. Technol., December, 2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification.
Mach. Learn. Sci. Technol., 2022

Neural Inference of Gaussian Processes for Time Series Data of Quasars.
CoRR, 2022

Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection.
CoRR, 2022

Interpretable Uncertainty Quantification in AI for HEP.
CoRR, 2022

Discovering the building blocks of dark matter halo density profiles with neural networks.
CoRR, 2022

Learning Representation for Bayesian Optimization with Collision-free Regularization.
CoRR, 2022

Machine Learning and Cosmology.
CoRR, 2022

DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images.
Astron. Comput., 2022

2021
Ten simple rules to cultivate transdisciplinary collaboration in data science.
PLoS Comput. Biol., 2021

Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms.
Mach. Learn. Sci. Technol., 2021

deeplenstronomy: A dataset simulation package for strong gravitational lensing.
J. Open Source Softw., 2021

SkyPy: A package for modelling the Universe.
J. Open Source Softw., 2021

Robustness of deep learning algorithms in astronomy - galaxy morphology studies.
CoRR, 2021

DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains.
CoRR, 2021

DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning.
CoRR, 2021

A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images.
Astron. Comput., 2021

2020
Deep learning insights into cosmological structure formation.
CoRR, 2020

Domain adaptation techniques for improved cross-domain study of galaxy mergers.
CoRR, 2020

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks.
Astron. Comput., 2020

Unsupervised Resource Allocation with Graph Neural Networks.
Proceedings of the NeurIPS 2020 Workshop on Pre-registration in Machine Learning, 2020

2019
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer.
CoRR, 2019

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan".
CoRR, 2019

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era.
CoRR, 2019

DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks.
Astron. Comput., 2019

Challenges and Approaches for Mining Astronomical Data and Complex Models.
Proceedings of the 2019 IEEE International Conference on Big Data, 2019

2016
SPOKES: An end-to-end simulation facility for spectroscopic cosmological surveys.
Astron. Comput., 2016

Crowdsourcing quality control for Dark Energy Survey images.
Astron. Comput., 2016


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