Search Results for author: W.

Found 2 papers, 2 papers with code

Substructure Aware Graph Neural Networks

1 code implementation • Proceedings of the AAAI Conference on Artificial Intelligence 2023 • Zeng, D., Liu, Chen, W., Zhou, L., Zhang, M., & Qu, H

Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.

Ranked #7 on Graph Regression on ZINC-500k

Graph Learning Graph Regression

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Cosmic Background Removal with Deep Neural Networks in SBND

1 code implementation • 2 Dec 2020 • SBND Collaboration, R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, G. Brandt, T. Brooks, F. Carneiro, Y. Chen, H. Chen, G. Chisnall, I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I., L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di Benedetto, A. Ereditato, J. Evans, C. Ezeribe, S. Fitzpatrick, T. Fleming, W. Foreman, D. Franco, I. Furic, P. Furmanski, S. Gao, D. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R., S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M., J. Kim, A. Kudryavtsev, J. Larkin, H. Lay, I. Lepetic, B., R. Littlejohn, W., C. Louis, A., A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, P. Méndez, T. Mettler, K. Mistry, A. Mogan, J. Molina, M. Mooney, L. Mora, C., A. Moura, J. Mousseau, A. Navrer-Agasson, F., J. Nicolas-Arnaldos, A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V., L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D., W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N., J., C. Spooner, M. Stancari, V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G., A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, A. Zglam

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded.

Semantic Segmentation Data Analysis, Statistics and Probability

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