Zobrazeno 241 - 250
of 33 130
pro vyhledávání: '"A, Gillis"'
Autor:
Barlek, Mark H., Gillis, David C., Egner, Simon A., Maragos, Sophia L., Karver, Mark R., Stupp, Samuel I., Tsihlis, Nick D., Kibbe, Melina R.
Publikováno v:
In Biomaterials December 2023 303
Autor:
Gillis, Brian T., McWood, Leanna M., Brigham, Emily F., Hinnant, J. Benjamin, El-Sheikh, Mona
Publikováno v:
In Sleep Health: Journal of the National Sleep Foundation December 2023 9(6):868-875
Autor:
Gillis, Andrea, Zheng-Pywell, Rui, McLeod, Chandler, Wang, Dezhi, Ness, John M., Guenter, Rachael, Whitt, Jason, Prolla, Tomas A., Chen, Herbert, Gonzalez, Manuel Lora, Rose, Bart, Lloyd, Ricardo V., Jaskula-Sztul, Renata, Lin, Diana
Publikováno v:
In Modern Pathology December 2023 36(12)
Publikováno v:
SIAM J. on Imaging Sciences 14 (4), pp. 1848-1889, 2021
In this work, we consider the problem of blind source separation (BSS) by departing from the usual linear model and focusing on the linear-quadratic (LQ) model. We propose two provably robust and computationally tractable algorithms to tackle this pr
Externí odkaz:
http://arxiv.org/abs/2011.11966
Publikováno v:
Machine Learning 111, pp. 4453-4495, 2022
Nonnegative least squares problems with multiple right-hand sides (MNNLS) arise in models that rely on additive linear combinations. In particular, they are at the core of most nonnegative matrix factorization algorithms and have many applications. T
Externí odkaz:
http://arxiv.org/abs/2011.11066
Publikováno v:
SIAM J. on Matrix Analysis and its Applications 42 (2), 730-752, 2021
Nonnegative matrix factorization (NMF) is the problem of approximating an input nonnegative matrix, $V$, as the product of two smaller nonnegative matrices, $W$ and $H$. In this paper, we introduce a general framework to design multiplicative updates
Externí odkaz:
http://arxiv.org/abs/2010.16223
Autor:
Martinelli, M., Tutusaus, I., Archidiacono, M., Camera, S., Cardone, V. F., Clesse, S., Casas, S., Casarini, L., Mota, D. F., Hoekstra, H., Carbone, C., Ilić, S., Kitching, T. D., Pettorino, V., Pourtsidou, A., Sakr, Z., Sapone, D., Auricchio, N., Balestra, A., Boucaud, A., Branchini, E., Brescia, M., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C., Conversi, L., Corcione, L., Costille, A., Douspis, M., Dubath, F., Dusini, S., Fabbian, G., Fosalba, P., Frailis, M., Franceschi, E., Gillis, B., Giocoli, C., Grupp, F., Guzzo, L., Holmes, W., Hormuth, F., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Massey, R., Meneghetti, M., Meylan, G., Morin, B., Moscardini, L., Niemi, S., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pires, S., Polenta, G., Poncet, M., Popa, L., Raison, F., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Starck, J. -L., Sureau, F., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Welikala, N., Zacchei, A., Zoubian, J.
Upcoming surveys will map the growth of large-scale structure with unprecented precision, improving our understanding of the dark sector of the Universe. Unfortunately, much of the cosmological information is encoded by the small scales, where the cl
Externí odkaz:
http://arxiv.org/abs/2010.12382
Publikováno v:
Journal on Machine Learning Research 24 (18), pp. 1-41, 2023
In this paper, we introduce TITAN, a novel inerTIal block majorizaTion minimizAtioN framework for non-smooth non-convex optimization problems. To the best of our knowledge, TITAN is the first framework of block-coordinate update method that relies on
Externí odkaz:
http://arxiv.org/abs/2010.12133
Autor:
Hien, Le Thi Khanh, Gillis, Nicolas
Publikováno v:
Journal of Scientific Computing 87, 93, 2021
Nonnegative matrix factorization (NMF) is a standard linear dimensionality reduction technique for nonnegative data sets. In order to measure the discrepancy between the input data and the low-rank approximation, the Kullback-Leibler (KL) divergence
Externí odkaz:
http://arxiv.org/abs/2010.01935
Publikováno v:
Computer Science Review 42, 100423, 2021
Constrained low-rank matrix approximations have been known for decades as powerful linear dimensionality reduction techniques to be able to extract the information contained in large data sets in a relevant way. However, such low-rank approaches are
Externí odkaz:
http://arxiv.org/abs/2010.00380