Zobrazeno 1 - 10
of 15 717
pro vyhledávání: '"Belkin, A"'
Autor:
Killestein, T. L., Kelsey, L., Wickens, E., Nuttall, L., Lyman, J., Krawczyk, C., Ackley, K., Dyer, M. J., Jiménez-Ibarra, F., Ulaczyk, K., O'Neill, D., Kumar, A., Steeghs, D., Galloway, D. K., Dhillon, V. S., O'Brien, P., Ramsay, G., Noysena, K., Kotak, R., Breton, R. P., Pallé, E., Pollacco, D., Awiphan, S., Belkin, S., Chote, P., Clark, P., Coppejans, D., Duffy, C., Eyles-Ferris, R., Godson, B., Gompertz, B., Graur, O., Irawati, P., Jarvis, D., Julakanti, Y., Kennedy, M. R., Kuncarayakti, H., Levan, A., Littlefair, S., Magee, M., Mandhai, S., Sánchez, D. Mata, Mattila, S., McCormac, J., Mullaney, J., Munday, J., Patel, M., Pursiainen, M., Rana, J., Sawangwit, U., Stanway, E., Starling, R., Warwick, B., Wiersema, K.
Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratised, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this tor
Externí odkaz:
http://arxiv.org/abs/2406.02334
Autor:
Dainotti, M. G., De Simone, B., Malik, R. F. Mohideen, Pasumarti, V., Levine, D., Saha, N., Gendre, B., Kido, D., Watson, A. M., Becerra, R. L., Belkin, S., Desai, S., Pedreira, A. C. C. do E. S., Das, U., Li, L., Oates, S. R., Cenko, S. B., Pozanenko, A., Volnova, A., Hu, Y. -D., Castro-Tirado, A. J., Orange, N. B., Moriya, T. J., Fraija, N., Niino, Y., Rinaldi, E., Butler, N. R., Gonzalez, J. d. J. G., Kutyrev, A. S., Lee, W. H., Prochaska, X., Ramirez-Ruiz, E., Richer, M., Siegel, M. H., Misra, K., Rossi, A., Lopresti, C., Quadri, U., Strabla, L., Ruocco, N., Leonini, S., Conti, M., Rosi, P., Ramirez, L. M. T., Zola, S., Jindal, I., Kumar, R., Chan, L., Fuentes, M., Lambiase, G., Kalinowski, K. K., Jamal, W.
We present the largest optical photometry compilation of Gamma-Ray Bursts (GRBs) with redshifts ($z$). We include 64813 observations of 535 events (including upper limits) from 28 February 1997 up to 18 August 2023. We also present a user-friendly we
Externí odkaz:
http://arxiv.org/abs/2405.02263
Autor:
Cotrufo, Michele, Krakofsky, Jonas, Mann, Sander A., Böhm, Gerhard, Belkin, Mikhail A., Alù, Andrea
Nonlinear intersubband polaritonic metasurfaces support one of the strongest known ultrafast nonlinear responses in the mid-infrared frequency range across all condensed matter systems. Beyond harmonic generation and frequency mixing, these nonlinear
Externí odkaz:
http://arxiv.org/abs/2403.15911
Deep Neural Collapse (DNC) refers to the surprisingly rigid structure of the data representations in the final layers of Deep Neural Networks (DNNs). Though the phenomenon has been measured in a variety of settings, its emergence is typically explain
Externí odkaz:
http://arxiv.org/abs/2402.13728
While displaying impressive generation capabilities across many tasks, Large Language Models (LLMs) still struggle with crucial issues of privacy violation and unwanted exposure of sensitive data. This raises an essential question: how should we prev
Externí odkaz:
http://arxiv.org/abs/2402.10052
A fundamental problem in machine learning is to understand how neural networks make accurate predictions, while seemingly bypassing the curse of dimensionality. A possible explanation is that common training algorithms for neural networks implicitly
Externí odkaz:
http://arxiv.org/abs/2401.04553
Autor:
Belkin, S., Pozanenko, A. S., Minaev, P. Y., Pankov, N. S., Volnova, A. A., Rossi, A., Stratta, G., Benetti, S., Palazzi, E., Moskvitin, A. S., Burhonov, O., Rumyantsev, V. V., Klunko, E. V., Inasaridze, R. Ya., Reva, I. V., Kim, V., Jelinek, M., Kann, D. A., Volvach, A. E., Volvach, L. N., Xu, D., Zhu, Z., Fu, S., Mkrtchyan, A. A.
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Volume 527, Issue 4, February 2024, Pages 11507-11520
We present full photometric coverage and spectroscopic data for soft GRB 201015A with a redshift z = 0.426. Our data spans a time range of 85 days following the detection of GRB. These observations revealed an underlying supernova SN 201015A with a m
Externí odkaz:
http://arxiv.org/abs/2401.03579
Kernel methods are a popular class of nonlinear predictive models in machine learning. Scalable algorithms for learning kernel models need to be iterative in nature, but convergence can be slow due to poor conditioning. Spectral preconditioning is an
Externí odkaz:
http://arxiv.org/abs/2312.03311
In our era of enormous neural networks, empirical progress has been driven by the philosophy that more is better. Recent deep learning practice has found repeatedly that larger model size, more data, and more computation (resulting in lower training
Externí odkaz:
http://arxiv.org/abs/2311.14646
Autor:
Yao, Kan, Fang, Jie, Jiang, Taizhi, Briggs, Andrew F., Skipper, Alec M., Kim, Youngsun, Belkin, Mikhail A., Korgel, Brian A., Bank, Seth R., Zheng, Yuebing
Optically resonant particles are key building blocks of many nanophotonic devices such as optical antennas and metasurfaces. Because the functionalities of such devices are largely determined by the optical properties of individual resonators, extend
Externí odkaz:
http://arxiv.org/abs/2311.06488