Zobrazeno 1 - 10
of 1 633
pro vyhledávání: '"Cundy, P."'
Autor:
Taufeeque, Mohammad, Quirke, Philip, Li, Maximilian, Cundy, Chris, Tucker, Aaron David, Gleave, Adam, Garriga-Alonso, Adrià
How a neural network (NN) generalizes to novel situations depends on whether it has learned to select actions heuristically or via a planning process. "An investigation of model-free planning" (Guez et al. 2019) found that a recurrent NN (RNN) traine
Externí odkaz:
http://arxiv.org/abs/2407.15421
Autor:
Collaboration, NA48/2, Batley, J. R., Kalmus, G., Lazzeroni, C., Munday, D. J., Slater, M. W., Wotton, S. A., Arcidiacono, R., Ceccucci, A., Bocquet, G., Cabibbo, N., Cundy, D., Falaleev, V., Gatignon, L., Fidecaro, M., Gonidec, A., Kubischta, W., Maier, A., Norton, A., Patel, M., Peters, A., Balev, S., Frabetti, P. L., Gersabeck, E., Goudzovski, E., Hristov, P., Kekelidze, V., Madigozhin, D., Molokanova, N., Polenkevich, I., Potrebenikov, Yu., Korotkova, A., Stoynev, S., Zinchenko, A., Kozhuharov, V., Litov, L., Monnier, E., Swallow, E., Winston, R., Rubin, P., Walker, A., Baldini, W., Ramusino, A. Cotta, Dalpiaz, P., Damiani, C., Fiorini, M., Gianoli, A., Martini, M., Petrucci, F., Savrié, M., Scarpa, M., Wahl, H., Bizzeti, A., Veltri, M., Calvetti, M., Celeghini, E., Iacopini, E., Lenti, M., Ruggiero, G., Behler, M., Eppard, K., Hita-Hochgesand, M., Kleinknecht, K., Marouelli, P., Masetti, L., Moosbrugger, U., Morales, C. Morales, Renk, B., Wache, M., Winhart, A., Wanke, R., Coward, D., Dabrowski, A., Martin, T. Fonseca, Shieh, M., Szleper, M., Velasco, M., Wood, M. D., Cenci, P., Pepe, M., Petrucci, M. C., Anzivino, G., Imbergamo, E., Nappi, A., Piccini, M., Raggi, M., Valdata-Nappi, M., Cerri, C., Fantechi, R., Collazuol, G., Di Lella, L., Lamanna, G., Mannelli, I., Michetti, A., Costantini, F., Doble, N., Fiorini, L., Giudici, S., Pierazzini, G., Sozzi, M., Venditti, S., Bloch-Devaux, B., Peyaud, B., Cheshkov, C., Chèze, J. B., De Beer, M., Derré, J., Marel, G., Mazzucato, E., Vallage, B., Holder, M., Ziolkowski, M., Biino, C., Cartiglia, N., Marchetto, F., Bifani, S., Clemencic, M., Lopez, S. Goy, Dibon, H., Jeitler, M., Markytan, M., Mikulec, I., Neuhofer, G., Widhalm, L.
Publikováno v:
JHEP 03(2024)137
The NA48/2 experiment at CERN reports the first observation of the $K^{\pm} \rightarrow \pi^{0} \pi^{0} \mu^{\pm} \nu$ decay based on a sample of 2437 candidates with 15% background contamination collected in 2003--2004. The decay branching ratio in
Externí odkaz:
http://arxiv.org/abs/2310.20295
Autor:
Hu, Yingjie, Mai, Gengchen, Cundy, Chris, Choi, Kristy, Lao, Ni, Liu, Wei, Lakhanpal, Gaurish, Zhou, Ryan Zhenqi, Joseph, Kenneth
Publikováno v:
International Journal of Geographical Information Science, 2023
Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many of these location descriptions go beyond simple place names, such as
Externí odkaz:
http://arxiv.org/abs/2310.09340
Autor:
Cundy, Chris, Ermon, Stefano
In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximum-likelihood (MLE) objective does not necessarily match a downstream use-case of autoregressively generating high-qu
Externí odkaz:
http://arxiv.org/abs/2306.05426
Autor:
Mai, Gengchen, Huang, Weiming, Sun, Jin, Song, Suhang, Mishra, Deepak, Liu, Ninghao, Gao, Song, Liu, Tianming, Cong, Gao, Hu, Yingjie, Cundy, Chris, Li, Ziyuan, Zhu, Rui, Lao, Ni
Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their succ
Externí odkaz:
http://arxiv.org/abs/2304.06798
Particularly in low-data regimes, an outstanding challenge in machine learning is developing principled techniques for augmenting our models with suitable priors. This is to encourage them to learn in ways that are compatible with our understanding o
Externí odkaz:
http://arxiv.org/abs/2210.12530
Autor:
Grau-Moya, Jordi, Delétang, Grégoire, Kunesch, Markus, Genewein, Tim, Catt, Elliot, Li, Kevin, Ruoss, Anian, Cundy, Chris, Veness, Joel, Wang, Jane, Hutter, Marcus, Summerfield, Christopher, Legg, Shane, Ortega, Pedro
Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rather than an explicit modeling assumption. Bayes-optimal agents are risk
Externí odkaz:
http://arxiv.org/abs/2209.15618
Autor:
Delétang, Grégoire, Ruoss, Anian, Grau-Moya, Jordi, Genewein, Tim, Wenliang, Li Kevin, Catt, Elliot, Cundy, Chris, Hutter, Marcus, Legg, Shane, Veness, Joel, Ortega, Pedro A.
Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field. In this work, we conduct an extensive empirical study (20'91
Externí odkaz:
http://arxiv.org/abs/2207.02098
Autor:
Thomas J. Williams, David Blockley, Andrew B. Cundy, Jasmin A. Godbold, Rebecca M. Howman, Martin Solan
Publikováno v:
Ecology and Evolution, Vol 14, Iss 7, Pp n/a-n/a (2024)
Abstract Multiple expressions of climate change, in particular warming‐induced reductions in the type, extent and thickness of sea ice, are opening access and providing new viable development opportunities in high‐latitude regions. Coastal margin
Externí odkaz:
https://doaj.org/article/e5486db3b1e84fd7be30ba9c09787d65
Autor:
The NA48/2 collaboration, J. R. Batley, G. Kalmus, C. Lazzeroni, D. J. Munday, M. W. Slater, S. A. Wotton, R. Arcidiacono, G. Bocquet, N. Cabibbo, A. Ceccucci, D. Cundy, V. Falaleev, M. Fidecaro, L. Gatignon, A. Gonidec, W. Kubischta, A. Maier, A. Norton, M. Patel, A. Peters, E. Monnier, E. Swallow, R. Winston, P. Rubin, A. Walker, P. Dalpiaz, C. Damiani, M. Fiorini, M. Martini, F. Petrucci, M. Savrié, M. Scarpa, H. Wahl, W. Baldini, A. Cotta Ramusino, A. Gianoli, M. Calvetti, E. Celeghini, E. Iacopini, M. Lenti, G. Ruggiero, A. Bizzeti, M. Veltri, M. Behler, K. Eppard, M. Hita-Hochgesand, K. Kleinknecht, P. Marouelli, L. Masetti, U. Moosbrugger, C. Morales Morales, B. Renk, M. Wache, R. Wanke, A. Winhart, D. Coward, A. Dabrowski, T. Fonseca Martin, M. Shieh, M. Szleper, M. Velasco, M. D. Wood, G. Anzivino, E. Imbergamo, A. Nappi, M. Piccini, M. Raggi, M. Valdata-Nappi, P. Cenci, M. Pepe, M. C. Petrucci, F. Costantini, N. Doble, L. Fiorini, S. Giudici, G. Pierazzini, M. Sozzi, S. Venditti, G. Collazuol, L. Di Lella, G. Lamanna, I. Mannelli, A. Michetti, C. Cerri, R. Fantechi, B. Bloch-Devaux, C. Cheshkov, J. B. Chèze, M. De Beer, J. Derré, G. Marel, E. Mazzucato, B. Peyaud, B. Vallage, M. Holder, M. Ziolkowski, S. Bifani, M. Clemencic, S. Goy Lopez, C. Biino, N. Cartiglia, F. Marchetto, H. Dibon, M. Jeitler, M. Markytan, I. Mikulec, G. Neuhofer, L. Widhalm, S. Balev, P. L. Frabetti, E. Gersabeck, E. Goudzovski, P. Hristov, V. Kekelidze, A. Korotkova, V. Kozhuharov, L. Litov, D. Madigozhin, N. Molokanova, I. Polenkevich, Yu. Potrebenikov, S. Stoynev, A. Zinchenko
Publikováno v:
Journal of High Energy Physics, Vol 2024, Iss 3, Pp 1-18 (2024)
Abstract The NA48/2 experiment at CERN reports the first observation of the K ± → π 0 π 0 μ ± ν decay based on a sample of 2437 candidates with 15% background contamination collected in 2003–2004. The decay branching ratio in the kinematic
Externí odkaz:
https://doaj.org/article/c444042736794b469d4efa4cddbb6658