Zobrazeno 1 - 10
of 3 636
pro vyhledávání: '"Schein, P."'
Publikováno v:
IEPC-2024-469
Non-destructive X-ray imaging of thruster parts and assemblies down to the scale of several micrometers is a key technology for electric propulsion research and engineering. It allows for thorough product assurance, rapid state acquisition and implem
Externí odkaz:
http://arxiv.org/abs/2412.04214
The ion-optic grid-system is the essential part of electrostatic ion thrusters governing performance and lifetime. Therefore reliable measurements of the grid and aperture geometry over the lifetime are necessary to understand and predict the behavio
Externí odkaz:
http://arxiv.org/abs/2412.03426
We consider the problem of developing interpretable and computationally efficient matrix decomposition methods for matrices whose entries have bounded support. Such matrices are found in large-scale DNA methylation studies and many other settings. Ou
Externí odkaz:
http://arxiv.org/abs/2410.18425
Autor:
Stoehr, Niklas, Du, Kevin, Snæbjarnarson, Vésteinn, West, Robert, Cotterell, Ryan, Schein, Aaron
Given the prompt "Rome is in", can we steer a language model to flip its prediction of an incorrect token "France" to a correct token "Italy" by only multiplying a few relevant activation vectors with scalars? We argue that successfully intervening o
Externí odkaz:
http://arxiv.org/abs/2410.04962
Nanoscale magnetic resonance imaging (nanoMRI) aims at obtaining structure at the single molecule level. Most of the techniques for effecting a nanoMRI gradient use small permanent magnets. Here, we present a switchable magnetic field gradient on a t
Externí odkaz:
http://arxiv.org/abs/2409.17690
Racial and other demographic imputation is necessary for many applications, especially in auditing disparities and outreach targeting in political campaigns. The canonical approach is to construct continuous predictions -- e.g., based on name and geo
Externí odkaz:
http://arxiv.org/abs/2405.16762
Autor:
Du, Kevin, Snæbjarnarson, Vésteinn, Stoehr, Niklas, White, Jennifer C., Schein, Aaron, Cotterell, Ryan
To answer a question, language models often need to integrate prior knowledge learned during pretraining and new information presented in context. We hypothesize that models perform this integration in a predictable way across different questions and
Externí odkaz:
http://arxiv.org/abs/2404.04633
Autor:
Springer, O., Ofek, E. O., Zackay, B., Konno, R., Sharon, A., Nir, G., Rubin, A., Haddad, A., Friedman, J., Lubomirsky, L. Schein, Aizenberg, I., Krassilchtchikov, A., Gal-Yam, A.
Detection of moving sources over complicated background is important for several reasons. First is measuring the astrophysical motion of the source. Second is that such motion resulting from atmospheric scintillation, color refraction, or astrophysic
Externí odkaz:
http://arxiv.org/abs/2403.09771
Autor:
Hood, John, Schein, Aaron
This paper introduces AL$\ell_0$CORE, a new form of probabilistic non-negative tensor decomposition. AL$\ell_0$CORE is a Tucker decomposition where the number of non-zero elements (i.e., the $\ell_0$-norm) of the core tensor is constrained to a prese
Externí odkaz:
http://arxiv.org/abs/2403.06153
Autor:
O'Hagan, Sean, Schein, Aaron
Much of social science is centered around terms like ``ideology'' or ``power'', which generally elude precise definition, and whose contextual meanings are trapped in surrounding language. This paper explores the use of large language models (LLMs) t
Externí odkaz:
http://arxiv.org/abs/2312.09203