Zobrazeno 1 - 10
of 4 515
pro vyhledávání: '"Perlmutter, P."'
Autor:
Viswanath, Siddharth, Bhaskar, Dhananjay, Johnson, David R., Rocha, Joao Felipe, Castro, Egbert, Grady, Jackson D., Grigas, Alex T., Perlmutter, Michael A., O'Hern, Corey S., Krishnaswamy, Smita
Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond
Externí odkaz:
http://arxiv.org/abs/2410.20317
Autor:
Johnson, David R., Chew, Joyce, Viswanath, Siddharth, De Brouwer, Edward, Needell, Deanna, Krishnaswamy, Smita, Perlmutter, Michael
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine framework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggest
Externí odkaz:
http://arxiv.org/abs/2410.14639
Autor:
Sun, Xingzhi, Xu, Charles, Rocha, João F., Liu, Chen, Hollander-Bodie, Benjamin, Goldman, Laney, DiStasio, Marcello, Perlmutter, Michael, Krishnaswamy, Smita
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergraphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerfu
Externí odkaz:
http://arxiv.org/abs/2409.09469
Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction. However, the use of GNNs (and machine learning more generally) to solve combinatorial optimization
Externí odkaz:
http://arxiv.org/abs/2405.20543
Autor:
Morrell, N., Phillips, M. M., Folatelli, G., Stritzinger, M. D., Hamuy, M., Suntzeff, N. B., Hsiao, E. Y., Taddia, F., Burns, C. R., Hoeflich, P., Ashall, C., Contreras, C., Galbany, L., Lu, J., Piro, A. L., Anais, J., Baron, E., Burrow, A., Busta, L., Campillay, A., Castellón, S., Corco, C., Diamond, T., Freedman, W. L., González, C., Krisciunas, K., Kumar, S., Persson, S. E., Serón, J., Shahbandeh, M., Torres, S., Uddin, S. A., Anderson, J. P., Baltay, C., Gall, C., Goobar, A., Hadjiyska, E., Holmbo, S., Kasliwal, M., Lidman, C., Marion, G. H., Mazzali, P., Nugent, P., Perlmutter, S., Pignata, G., Rabinowitz, D., Roth, M., Ryder, S. D., Shappee, B. J., Vinkó, J., Wheeler, J. C., de Jaeger, T., Lira, P., Ruiz, M. T., Rich, J. A., Prieto, J. L., Di Mille, F., Osip, D., Blanc, G., Palunas, P.
We present the second and final release of optical spectroscopy of Type Ia Supernovae (SNe Ia) obtained during the first and second phases of the Carnegie Supernova Project (CSP-I and CSP-II). The newly released data consist of 148 spectra of 30 SNe
Externí odkaz:
http://arxiv.org/abs/2404.19208
Autor:
Perlmutter, Eric
We give an elementary proof of the following property of unitary, interacting four-dimensional $\mathcal{N}=2$ superconformal field theories: at large central charge $c$, there exist at least $\sqrt{c}$ single-trace, scalar superconformal primary ope
Externí odkaz:
http://arxiv.org/abs/2402.19358
Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its struc
Externí odkaz:
http://arxiv.org/abs/2311.16378
Autor:
Rubin, David, Aldering, Greg, Betoule, Marc, Fruchter, Andy, Huang, Xiaosheng, Kim, Alex G., Lidman, Chris, Linder, Eric, Perlmutter, Saul, Ruiz-Lapuente, Pilar, Suzuki, Nao
Type Ia supernovae (SNe Ia) were instrumental in establishing the acceleration of the universe's expansion. By virtue of their combination of distance reach, precision, and prevalence, they continue to provide key cosmological constraints, complement
Externí odkaz:
http://arxiv.org/abs/2311.12098
Autor:
Xu, Charles, Goldman, Laney, Guo, Valentina, Hollander-Bodie, Benjamin, Trank-Greene, Maedee, Adelstein, Ian, De Brouwer, Edward, Ying, Rex, Krishnaswamy, Smita, Perlmutter, Michael
Publikováno v:
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:4537-4545, 2024
Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much less work has been done on signal classification, where the data consists of many functions (referred to as sig
Externí odkaz:
http://arxiv.org/abs/2310.17579
Autor:
Bhaskar, Dhananjay, Zhang, Yanlei, Xu, Charles, Sun, Xingzhi, Fasina, Oluwadamilola, Wolf, Guy, Nickel, Maximilian, Perlmutter, Michael, Krishnaswamy, Smita
In this paper we introduce DYMAG: a message passing paradigm for GNNs built on the expressive power of continuous, multiscale graph-dynamics. Standard discrete-time message passing algorithms implicitly make use of simplistic graph dynamics and aggre
Externí odkaz:
http://arxiv.org/abs/2309.09924