no code implementations • 17 Jul 2023 • C. Allaire, R. Ammendola, E. -C. Aschenauer, M. Balandat, M. Battaglieri, J. Bernauer, M. Bondì, N. Branson, T. Britton, A. Butter, I. Chahrour, P. Chatagnon, E. Cisbani, E. W. Cline, S. Dash, C. Dean, W. Deconinck, A. Deshpande, M. Diefenthaler, R. Ent, C. Fanelli, M. Finger, M. Finger, Jr., E. Fol, S. Furletov, Y. Gao, J. Giroux, N. C. Gunawardhana Waduge, R. Harish, O. Hassan, P. L. Hegde, R. J. Hernández-Pinto, A. Hiller Blin, T. Horn, J. Huang, D. Jayakodige, B. Joo, M. Junaid, P. Karande, B. Kriesten, R. Kunnawalkam Elayavalli, M. Lin, F. Liu, S. Liuti, G. Matousek, M. McEneaney, D. McSpadden, T. Menzo, T. Miceli, V. Mikuni, R. Montgomery, B. Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, G. N. Perdue, W. Phelps, M. L. Purschke, K. Rajput, Y. Ren, D. F. Renteria-Estrada, D. Richford, B. J. Roy, D. Roy, N. Sato, T. Satogata, G. Sborlini, M. Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, A. -N. Tawfik, F. Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, A. Vossen, E. Walter, D. Whiteson, M. Williams, S. Wu, N. Zachariou, P. Zurita
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028.
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.
no code implementations • 5 Nov 2019 • J. Amundson, J. Annis, C. Avestruz, D. Bowring, J. Caldeira, G. Cerati, C. Chang, S. Dodelson, D. Elvira, A. Farahi, K. Genser, L. Gray, O. Gutsche, P. Harris, J. Kinney, J. B. Kowalkowski, R. Kutschke, S. Mrenna, B. Nord, A. Para, K. Pedro, G. N. Perdue, A. Scheinker, P. Spentzouris, J. St. John, N. Tran, S. Trivedi, L. Trouille, W. L. K. Wu, C. R. Bom
Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership.