Zobrazeno 1 - 10
of 10 629
pro vyhledávání: '"P Shay"'
Autor:
Lack, Stephen, Tobin, Shay
The categories of real and of complex Hilbert spaces with bounded linear maps have received purely categorical characterisations by Chris Heunen and Andre Kornell. These characterisations are achieved through Sol\`er's theorem, a result which shows t
Externí odkaz:
http://arxiv.org/abs/2412.03776
In India, the majority of farmers are classified as small or marginal, making their livelihoods particularly vulnerable to economic losses due to market saturation and climate risks. Effective crop planning can significantly impact their expected inc
Externí odkaz:
http://arxiv.org/abs/2412.02057
Classic supervised learning involves algorithms trained on $n$ labeled examples to produce a hypothesis $h \in \mathcal{H}$ aimed at performing well on unseen examples. Meta-learning extends this by training across $n$ tasks, with $m$ examples per ta
Externí odkaz:
http://arxiv.org/abs/2411.17898
Autor:
Ben-Moshe, Shay
We prove an ambidexterity result for $\infty$-categories of $\infty$-categories admitting a collection of colimits. This unifies and extends two known phenomena: the identification of limits and colimits of presentable $\infty$-categories indexed by
Externí odkaz:
http://arxiv.org/abs/2411.17281
Autor:
Derei, Nitay, Balberg, Shmuel, Heizler, Shay I., Steinberg, Elad, McClarren, Ryan G., Krief, Menahem
We derive a family of similarity solutions to the nonlinear non-equilibrium Marshak wave problem for an inhomogeneous planar medium which is coupled to a time dependent radiation driving source. We employ the non-equilibrium gray diffusion approximat
Externí odkaz:
http://arxiv.org/abs/2411.14891
Precision and Recall are foundational metrics in machine learning where both accurate predictions and comprehensive coverage are essential, such as in recommender systems and multi-label learning. In these tasks, balancing precision (the proportion o
Externí odkaz:
http://arxiv.org/abs/2411.13029
Traditional Learning-To-Rank (LETOR) approaches, including pairwise methods like RankNet and LambdaMART, often fall short by solely focusing on pairwise comparisons, leading to sub-optimal global rankings. Conversely, deep learning based listwise met
Externí odkaz:
http://arxiv.org/abs/2411.12064
Many practical prediction algorithms represent inputs in Euclidean space and replace the discrete 0/1 classification loss with a real-valued surrogate loss, effectively reducing classification tasks to stochastic optimization. In this paper, we inves
Externí odkaz:
http://arxiv.org/abs/2411.10784
Autor:
Figaredo, Catalina Sobrino, Chelouche, Doron, Haas, Martin, Ramolla, Michael, Kaspi, Shai, Panda, Swayamtrupta, Ochmann, Martin W., Zucker, Shay, Chini, Rolf, Probst, Malte A., Kollatschny, Wolfram, Murphy, Miguel
We present the results of a nearly decade-long photometric reverberation mapping (PRM) survey of the H$\alpha$ emission line in nearby ($0.01\lesssim z \lesssim0.05$) Seyfert-Galaxies using small ($15\,\mathrm{cm}-40\,\mathrm{cm}$) telescopes. Broad-
Externí odkaz:
http://arxiv.org/abs/2411.07847
We introduce efficient differentially private (DP) algorithms for several linear algebraic tasks, including solving linear equalities over arbitrary fields, linear inequalities over the reals, and computing affine spans and convex hulls. As an applic
Externí odkaz:
http://arxiv.org/abs/2411.03087