Zobrazeno 1 - 10
of 995
pro vyhledávání: '"Caterini A"'
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
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
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Autor:
Stein, George, Cresswell, Jesse C., Hosseinzadeh, Rasa, Sui, Yi, Ross, Brendan Leigh, Villecroze, Valentin, Liu, Zhaoyan, Caterini, Anthony L., Taylor, J. Eric T., Loaiza-Ganem, Gabriel
Publikováno v:
Thirty-seventh Conference on Neural Information Processing Systems (2023)
We systematically study a wide variety of generative models spanning semantically-diverse image datasets to understand and improve the feature extractors and metrics used to evaluate them. Using best practices in psychophysics, we measure human perce
Externí odkaz:
http://arxiv.org/abs/2306.04675
Publikováno v:
Canadian Urological Association Journal. Oct2024, Vol. 18 Issue 10, p329-332. 4p.
Autor:
Loaiza-Ganem, Gabriel, Ross, Brendan Leigh, Wu, Luhuan, Cunningham, John P., Cresswell, Jesse C., Caterini, Anthony L.
Likelihood-based deep generative models have recently been shown to exhibit pathological behaviour under the manifold hypothesis as a consequence of using high-dimensional densities to model data with low-dimensional structure. In this paper we propo
Externí odkaz:
http://arxiv.org/abs/2212.01265