Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Brendan Leigh Ross"'
Publikováno v:
Jesse Cresswell
Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypothesis, which states that observed data lies on a low-dimensional manif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb4e29f540015507705c411eb7a26972
http://arxiv.org/abs/2204.07172
http://arxiv.org/abs/2204.07172
Publikováno v:
Jesse Cresswell
Natural data observed in $\mathbb{R}^n$ is often constrained to an $m$-dimensional manifold $\mathcal{M}$, where $m < n$. Current generative models represent this manifold by mapping an $m$-dimensional latent variable through a neural network $f_\the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff78be8acb7ec66c0328b060537e69a4
Publikováno v:
Jesse Cresswell
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee3fada334635175100d86e07d3ad89a
Autor:
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, Cunningham, John P., Jesse Cresswell, Caterini, Anthony L.
Publikováno v:
Jesse Cresswell
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbd2eba44057a9df9cc3068a3088769d
Autor:
Brendan Leigh Ross, Jesse Cresswell
Publikováno v:
Jesse Cresswell
Normalizing flows are generative models that provide tractable density estimation via an invertible transformation from a simple base distribution to a complex target distribution. However, this technique cannot directly model data supported on an un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f24a9f1961d9201864c8e425793277ea
Autor:
Brown, Bradley C. A., Caterini, Anthony L., Brendan Leigh Ross, Jesse Cresswell, Gabriel Loaiza-Ganem
Publikováno v:
Jesse Cresswell
Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the man
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::143a13ebdbf6874c11c9c0935f8b7aec
https://openreview.net/forum?id=Rvee9CAX4fi
https://openreview.net/forum?id=Rvee9CAX4fi
Autor:
Jesse Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez, Marco Letizia, Caterini, Anthony L.
Publikováno v:
Marco Letizia
Jesse Cresswell
Jesse Cresswell
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f4e4c0fc3b521a60c5f0251ba2476c1
https://arxiv.org/abs/2211.15380
https://arxiv.org/abs/2211.15380