Zobrazeno 1 - 10
of 20 920
pro vyhledávání: '"Stanislas, A"'
Autor:
Glazer, Elliot, Erdil, Ege, Besiroglu, Tamay, Chicharro, Diego, Chen, Evan, Gunning, Alex, Olsson, Caroline Falkman, Denain, Jean-Stanislas, Ho, Anson, Santos, Emily de Oliveira, Järviniemi, Olli, Barnett, Matthew, Sandler, Robert, Vrzala, Matej, Sevilla, Jaime, Ren, Qiuyu, Pratt, Elizabeth, Levine, Lionel, Barkley, Grant, Stewart, Natalie, Grechuk, Bogdan, Grechuk, Tetiana, Enugandla, Shreepranav Varma, Wildon, Mark
We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics -- from computationally intensiv
Externí odkaz:
http://arxiv.org/abs/2411.04872
We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-orde
Externí odkaz:
http://arxiv.org/abs/2408.13114
Autor:
Unser, Michael, Ducotterd, Stanislas
We first establish a kernel theorem that characterizes all linear shift-invariant (LSI) operators acting on discrete multicomponent signals. This result naturally leads to the identification of the Parseval convolution operators as the class of energ
Externí odkaz:
http://arxiv.org/abs/2408.09981
Autor:
Gopakumar, Vignesh, Gray, Ander, Oskarsson, Joel, Zanisi, Lorenzo, Pamela, Stanislas, Giles, Daniel, Kusner, Matt, Deisenroth, Marc Peter
Data-driven surrogate models have shown immense potential as quick, inexpensive approximations to complex numerical and experimental modelling tasks. However, most surrogate models of physical systems do not quantify their uncertainty, rendering thei
Externí odkaz:
http://arxiv.org/abs/2408.09881
In this work, we develop a differentiable rendering pipeline for visualising plasma emission within tokamaks, and estimating the gradients of the emission and estimating other physical quantities. Unlike prior work, we are able to leverage arbitrary
Externí odkaz:
http://arxiv.org/abs/2408.07555
Notwithstanding its relevance to many applications in sensing, security, and communications, electrical generation of narrow-band mid-infrared light remains highly challenging. Unlike in the ultraviolet or visible spectral regions few materials posse
Externí odkaz:
http://arxiv.org/abs/2407.00430
Autor:
Gopakumar, Vignesh, Oskarrson, Joel, Gray, Ander, Zanisi, Lorenzo, Pamela, Stanislas, Giles, Daniel, Kusner, Matt, Deisenroth, Marc
Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do not quantify the uncertainty associated with their forecasts. This lim
Externí odkaz:
http://arxiv.org/abs/2406.14483
Autor:
Agrawal, Aakash, Dehaene, Stanislas
Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate highly similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly ove
Externí odkaz:
http://arxiv.org/abs/2403.06159
Score-based generative models (SGMs) aim at estimating a target data distribution by learning score functions using only noise-perturbed samples from the target.Recent literature has focused extensively on assessing the error between the target and e
Externí odkaz:
http://arxiv.org/abs/2402.04650
We study the growth of the resolvent of a Toeplitz operator $T_b$, defined on the Hardy space, in terms of the distance to its spectrum $\sigma(T_b)$. We are primarily interested in the case when the symbol $b$ is a Laurent polynomial (\emph{i.e., }
Externí odkaz:
http://arxiv.org/abs/2401.12095