Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Jiang, Ruoxi"'
We introduce nested diffusion models, an efficient and powerful hierarchical generative framework that substantially enhances the generation quality of diffusion models, particularly for images of complex scenes. Our approach employs a series of diff
Externí odkaz:
http://arxiv.org/abs/2412.05984
Scientific modeling and engineering applications rely heavily on parameter estimation methods to fit physical models and calibrate numerical simulations using real-world measurements. In the absence of analytic statistical models with tractable likel
Externí odkaz:
http://arxiv.org/abs/2409.18402
We show that introducing a weighting factor to reduce the influence of identity shortcuts in residual networks significantly enhances semantic feature learning in generative representation learning frameworks, such as masked autoencoders (MAEs) and d
Externí odkaz:
http://arxiv.org/abs/2404.10947
Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neural operators trained to minimize squared error losses, while capable of
Externí odkaz:
http://arxiv.org/abs/2306.01187
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, 235, 2024, 38779-38814; https://proceedings.mlr.press/v235/orlova24a.html
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models. Unlike existing approaches, which exhibit computational co
Externí odkaz:
http://arxiv.org/abs/2305.19685
Autor:
Jiang, Ruoxi, Willett, Rebecca
This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems. We assume access to a computationally complex simulator that inputs a candidate parameter and outputs a corresponding m
Externí odkaz:
http://arxiv.org/abs/2211.01554
We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a
Externí odkaz:
http://arxiv.org/abs/2106.12034
Autor:
Zou, He, Zhu, Shunying, Jiang, Ruoxi, Chen, Qiucheng, Wu, Jingan, Wang, Pan, Diao, Chengliang
Publikováno v:
In Journal of Safety Research February 2023 84:280-289
Autor:
Jiang, Ruoxi1 (AUTHOR), Zhu, Shunying1 (AUTHOR), Wang, Pan1 (AUTHOR), Chen, QiuCheng1 (AUTHOR), Zou, He1 (AUTHOR), Kuang, Shiping1 (AUTHOR)
Publikováno v:
Journal of Advanced Transportation. 7/13/2020, p1-15. 15p.
Publikováno v:
Sustainability, Vol 13, Iss 9278, p 9278 (2021)
Sustainability
Volume 13
Issue 16
Sustainability
Volume 13
Issue 16
Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b6b85eea4261d2f5206f8bc2f6457bf
https://www.mdpi.com/2071-1050/13/16/9278
https://www.mdpi.com/2071-1050/13/16/9278