Zobrazeno 1 - 10
of 3 850
pro vyhledávání: '"Caterini A"'
Autor:
Pospíšil, Ctirad Václav1 cpospisil@volny.cz
Publikováno v:
Studia Theologica (1212570). 2019, Vol. 21 Issue 2, p75-95. 21p.
Autor:
Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t
Externí odkaz:
http://arxiv.org/abs/2410.21611
Autor:
Ma, Junwei, Thomas, Valentin, Hosseinzadeh, Rasa, Kamkari, Hamidreza, Labach, Alex, Cresswell, Jesse C., Golestan, Keyvan, Yu, Guangwei, Volkovs, Maksims, Caterini, Anthony L.
The challenges faced by neural networks on tabular data are well-documented and have hampered the progress of tabular foundation models. Techniques leveraging in-context learning (ICL) have shown promise here, allowing for dynamic adaptation to unsee
Externí odkaz:
http://arxiv.org/abs/2410.18164
Autor:
Pere Simón Castellano
Publikováno v:
Estudios de Deusto, Vol 69, Iss 2 (2021)
El Derecho, como reflejo de la sociedad que pretende regular, no puede quedar al margen de los cambios y transformaciones que en el seno de esta se producen. Luego las leyes son una suerte de traje a medida de la sociedad que pretenden regular; sólo
Externí odkaz:
https://doaj.org/article/0a18aeb675ea433fa7f17413d9ced19e
Advances in deep generative modelling have not translated well to tabular data. We argue that this is caused by a mismatch in structure between popular generative models and discriminative models of tabular data. We thus devise a technique to turn Ta
Externí odkaz:
http://arxiv.org/abs/2406.05216
Autor:
Thomas, Valentin, Ma, Junwei, Hosseinzadeh, Rasa, Golestan, Keyvan, Yu, Guangwei, Volkovs, Maksims, Caterini, Anthony
Tabular data is a pervasive modality spanning a wide range of domains, and the inherent diversity poses a considerable challenge for deep learning. Recent advancements using transformer-based in-context learning have shown promise on smaller and less
Externí odkaz:
http://arxiv.org/abs/2406.05207
Autor:
Loaiza-Ganem, Gabriel, Ross, Brendan Leigh, Hosseinzadeh, Rasa, Caterini, Anthony L., Cresswell, Jesse C.
In recent years there has been increased interest in understanding the interplay between deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses on understanding the reasons why commonly-used DGMs succeed or fail at l
Externí odkaz:
http://arxiv.org/abs/2404.02954
Autor:
Kamkari, Hamidreza, Ross, Brendan Leigh, Cresswell, Jesse C., Caterini, Anthony L., Krishnan, Rahul G., Loaiza-Ganem, Gabriel
Likelihood-based deep generative models (DGMs) commonly exhibit a puzzling behaviour: when trained on a relatively complex dataset, they assign higher likelihood values to out-of-distribution (OOD) data from simpler sources. Adding to the mystery, OO
Externí odkaz:
http://arxiv.org/abs/2403.18910
Foundation models have revolutionized tasks in computer vision and natural language processing. However, in the realm of tabular data, tree-based models like XGBoost continue to dominate. TabPFN, a transformer model tailored for tabular data, mirrors
Externí odkaz:
http://arxiv.org/abs/2402.06971
Publikováno v:
Canadian Urological Association Journal. Oct2024, Vol. 18 Issue 10, p329-332. 4p.