Zobrazeno 1 - 10
of 57 977
pro vyhledávání: '"A. Rosenbaum"'
In experimental design, aliasing of effects occurs in fractional factorial experiments, where certain low order factorial effects are indistinguishable from certain high order interactions: low order contrasts may be orthogonal to one another, while
Externí odkaz:
http://arxiv.org/abs/2408.16708
There is a large literature on the similarities and differences between biological neural circuits and deep artificial neural networks (DNNs). However, modern training of DNNs relies on several engineering tricks such as data batching, normalization,
Externí odkaz:
http://arxiv.org/abs/2408.08408
In applied time-to-event analysis, a flexible parametric approach is to model the hazard rate as a piecewise constant function of time. However, the change points and values of the piecewise constant hazard are usually unknown and need to be estimate
Externí odkaz:
http://arxiv.org/abs/2408.03602
Autor:
Stamp, P. C. E., Silevitch, D. M., Libersky, M., McKenzie, Ryan, Geim, A. A., Rosenbaum, T. F.
We examine the low-energy excitations in the vicinity of the quantum critical point in LiHoF$_4$, a physical realization of the Transverse Field Ising Model, focusing on the long-range fluctuations which soften to zero energy at the ferromagnetic qua
Externí odkaz:
http://arxiv.org/abs/2408.03510
We study the action of the Hecke operators $U_n$ on the space $\mathcal R$ of rational functions in one variable, over $\mathbb C$. The main goal is to give a complete classification of the eigenfunctions of $U_n$. We accomplish this by introducing c
Externí odkaz:
http://arxiv.org/abs/2406.15744
The emergence of Large Language Models (LLMs) with capabilities like In-Context Learning (ICL) has ushered in new possibilities for data generation across various domains while minimizing the need for extensive data collection and modeling techniques
Externí odkaz:
http://arxiv.org/abs/2404.09163
Underwater image restoration is a challenging task because of water effects that increase dramatically with distance. This is worsened by lack of ground truth data of clean scenes without water. Diffusion priors have emerged as strong image restorati
Externí odkaz:
http://arxiv.org/abs/2403.14837
In-context Learning (ICL) is the ability of Large Language Models (LLMs) to perform new tasks when conditioned on prompts comprising a few task examples. However, ICL performance can be critically sensitive to the choice of examples. To dynamically s
Externí odkaz:
http://arxiv.org/abs/2311.09606
We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility. Our asset class of interest is cryptocurrencies, at the beginning of the "crypto-winter" in 2022. We f
Externí odkaz:
http://arxiv.org/abs/2311.04727
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. In this paper, we propose a novel concept of transfer risk and and analyze its properties to e
Externí odkaz:
http://arxiv.org/abs/2311.03283