Zobrazeno 1 - 10
of 14 578
pro vyhledávání: '"P. Schaeffer"'
Single-operator learning involves training a deep neural network to learn a specific operator, whereas recent work in multi-operator learning uses an operator embedding structure to train a single neural network on data from multiple operators. Thus,
Externí odkaz:
http://arxiv.org/abs/2408.16168
Autor:
Griff-McMahon, J., Valenzuela-Villaseca, V., Malko, S., Fiksel, G., Rosenberg, M. J., Schaeffer, D. B., Fox, W.
Proton radiography is a central diagnostic technique for measuring electromagnetic (EM) fields in high-energy-density, laser-produced plasmas. In this technique, protons traverse the plasma where they accumulate small EM deflections which lead to var
Externí odkaz:
http://arxiv.org/abs/2408.10879
Autor:
Famà, Francesca, Zhou, Sheng, Heizenreder, Benedikt, Tang, Mikkel, Bennetts, Shayne, Jäger, Simon B., Schäffer, Stefan A., Schreck, Florian
Atoms coupled to cavities provide an exciting playground for the study of fundamental interactions of atoms mediated through a common channel. Many of the applications of cavity-QED and cold-atom experiments more broadly, suffer from limitations caus
Externí odkaz:
http://arxiv.org/abs/2407.18668
Autor:
Schaeffer, Rylan, Valentine, Dan, Bailey, Luke, Chua, James, Eyzaguirre, Cristóbal, Durante, Zane, Benton, Joe, Miranda, Brando, Sleight, Henry, Hughes, John, Agrawal, Rajashree, Sharma, Mrinank, Emmons, Scott, Koyejo, Sanmi, Perez, Ethan
The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesirable ways. In this work, we focus on a popular class of vision-languag
Externí odkaz:
http://arxiv.org/abs/2407.15211
Autor:
Reuel, Anka, Bucknall, Ben, Casper, Stephen, Fist, Tim, Soder, Lisa, Aarne, Onni, Hammond, Lewis, Ibrahim, Lujain, Chan, Alan, Wills, Peter, Anderljung, Markus, Garfinkel, Ben, Heim, Lennart, Trask, Andrew, Mukobi, Gabriel, Schaeffer, Rylan, Baker, Mauricio, Hooker, Sara, Solaiman, Irene, Luccioni, Alexandra Sasha, Rajkumar, Nitarshan, Moës, Nicolas, Ladish, Jeffrey, Guha, Neel, Newman, Jessica, Bengio, Yoshua, South, Tobin, Pentland, Alex, Koyejo, Sanmi, Kochenderfer, Mykel J., Trager, Robert
AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technic
Externí odkaz:
http://arxiv.org/abs/2407.14981
Autor:
Schaeffer, Joachim, Lenz, Eric, Gulla, Duncan, Bazant, Martin Z., Braatz, Richard D., Findeisen, Rolf
Health monitoring, fault analysis, and detection are critical for the safe and sustainable operation of battery systems. We apply Gaussian process resistance models on lithium iron phosphate battery field data to effectively separate the time-depende
Externí odkaz:
http://arxiv.org/abs/2406.19015
Palaeomagnetic evidence shows that the behaviour of the geodynamo has changed during geological times. Variations in the heat flux at the core-mantle boundary (CMB) due to mantle convection could be responsible. Previous studies, based on unrealistic
Externí odkaz:
http://arxiv.org/abs/2406.15083
Frontier AI systems are making transformative impacts across society, but such benefits are not without costs: models trained on web-scale datasets containing personal and private data raise profound concerns about data privacy and security. Language
Externí odkaz:
http://arxiv.org/abs/2406.14549
Autor:
Belkner, Sebastian, Duivenvoorden, Adriaan J., Carron, Julien, Schaeffer, Nathanael, Reinecke, Martin
We present $\texttt{cunusht}$, a general-purpose Python package that wraps a highly efficient CUDA implementation of the nonuniform spin-$0$ spherical harmonic transform. The method is applicable to arbitrary pixelization schemes, including schemes c
Externí odkaz:
http://arxiv.org/abs/2406.14542
In-context learning is a powerful capability of certain machine learning models that arguably underpins the success of today's frontier AI models. However, in-context learning is critically limited to settings where the in-context distribution of int
Externí odkaz:
http://arxiv.org/abs/2406.12785