1 code implementation • 3 Feb 2023 • A. Ćiprijanović, A. Lewis, K. Pedro, S. Madireddy, B. Nord, G. N. Perdue, S. M. Wild
This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps.
no code implementations • 1 Nov 2021 • A. Ćiprijanović, D. Kafkes, G. N. Perdue, K. Pedro, G. Snyder, F. J. Sánchez, S. Madireddy, S. M. Wild, B. Nord
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data.
1 code implementation • 2 Mar 2021 • A. Ćiprijanović, D. Kafkes, K. Downey, S. Jenkins, G. N. Perdue, S. Madireddy, T. Johnston, G. F. Snyder, B. Nord
Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)$-$ and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies.
no code implementations • 6 Nov 2020 • A. Ćiprijanović, D. Kafkes, S. Jenkins, K. Downey, G. N. Perdue, S. Madireddy, T. Johnston, B. Nord
In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations.