Zobrazeno 1 - 10
of 3 261
pro vyhledávání: '"Birrell, P"'
Autor:
Rafelski, Johann, Birrell, Jeremiah, Grayson, Christopher, Steinmetz, Andrew, Yang, Cheng Tao
We describe in the context of the particle physics (PP) standard model (SM) `PP-SM' the understanding of the primordial properties and composition of the Universe in the temperature range $130\GeV>T>20\keV$. The Universe evolution is described using
Externí odkaz:
http://arxiv.org/abs/2409.19031
Differentially private stochastic gradient descent (DP-SGD) has been instrumental in privately training deep learning models by providing a framework to control and track the privacy loss incurred during training. At the core of this computation lies
Externí odkaz:
http://arxiv.org/abs/2408.10456
Autor:
Birrell, Paul J, Blake, Joshua, Kandiah, Joel, Alexopoulos, Angelos, van Leeuwen, Edwin, Pouwels, Koen, Ghosh, Sanmitra, Starr, Colin, Walker, Ann Sarah, House, Thomas A, Gay, Nigel, Finnie, Thomas, Gent, Nick, Charlett, André, De Angelis, Daniela
A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstr
Externí odkaz:
http://arxiv.org/abs/2408.04178
Autor:
Birrell, Jeremiah
Generative adversarial networks (GANs) are unsupervised learning methods for training a generator distribution to produce samples that approximate those drawn from a target distribution. Many such methods can be formulated as minimization of a metric
Externí odkaz:
http://arxiv.org/abs/2406.16834
We present a novel method for training score-based generative models which uses nonlinear noising dynamics to improve learning of structured distributions. Generalizing to a nonlinear drift allows for additional structure to be incorporated into the
Externí odkaz:
http://arxiv.org/abs/2405.15625
We characterize in a novel manner the physical properties of the low temperature Fermi gas in the degenerate domain as a function of temperature and chemical potential. For the first time we obtain low temperature $T$ results in the domain where seve
Externí odkaz:
http://arxiv.org/abs/2405.05287
Likelihood-free inference methods based on neural conditional density estimation were shown to drastically reduce the simulation burden in comparison to classical methods such as ABC. When applied in the context of any latent variable model, such as
Externí odkaz:
http://arxiv.org/abs/2405.01737
Autor:
Birrell, Eleanor, Rodolitz, Jay, Ding, Angel, Lee, Jenna, McReynolds, Emily, Hutson, Jevan, Lerner, Ada
Growing recognition of the potential for exploitation of personal data and of the shortcomings of prior privacy regimes has led to the passage of a multitude of new online privacy regulations. Some of these laws -- notably the European Union's Genera
Externí odkaz:
http://arxiv.org/abs/2312.15383
Autor:
Charatan, Jan, Birrell, Eleanor
The California Privacy Rights Act (CPRA) was a ballot initiative that revised the California Consumer Privacy Act (CCPA). Although often framed as expanding and enhancing privacy rights, a close analysis of textual revisions -- both changes from the
Externí odkaz:
http://arxiv.org/abs/2312.15094
Privacy labels -- standardized, compact representations of data collection and data use practices -- are often presented as a solution to the shortcomings of privacy policies. Apple introduced mandatory privacy labels for apps in its App Store in Dec
Externí odkaz:
http://arxiv.org/abs/2312.03918