Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Ross, Brendan Leigh"'
Autor:
Vouitsis, Noël, Hosseinzadeh, Rasa, Ross, Brendan Leigh, Villecroze, Valentin, Gorti, Satya Krishna, Cresswell, Jesse C., Loaiza-Ganem, Gabriel
Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs) have recently emerged as a promising diffusion model distillation me
Externí odkaz:
http://arxiv.org/abs/2411.08954
Autor:
Ross, Brendan Leigh, Kamkari, Hamidreza, Wu, Tongzi, Hosseinzadeh, Rasa, Liu, Zhaoyan, Stein, George, Cresswell, Jesse C., Loaiza-Ganem, Gabriel
As deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the lega
Externí odkaz:
http://arxiv.org/abs/2411.00113
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:
Kamkari, Hamidreza, Ross, Brendan Leigh, Hosseinzadeh, Rasa, Cresswell, Jesse C., Loaiza-Ganem, Gabriel
High-dimensional data commonly lies on low-dimensional submanifolds, and estimating the local intrinsic dimension (LID) of a datum -- i.e. the dimension of the submanifold it belongs to -- is a longstanding problem. LID can be understood as the numbe
Externí odkaz:
http://arxiv.org/abs/2406.03537
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
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
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
Autor:
Cresswell, Jesse C., Ross, Brendan Leigh, Loaiza-Ganem, Gabriel, Reyes-Gonzalez, Humberto, Letizia, Marco, Caterini, Anthony L.
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most computationally expensive simulations involve calorimeter showers. Adva
Externí odkaz:
http://arxiv.org/abs/2211.15380
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted cen
Externí odkaz:
http://arxiv.org/abs/2210.06597