Zobrazeno 1 - 10
of 8 249
pro vyhledávání: '"GAO Rui"'
Autor:
Gao, Rui, Jaiman, Rajeev K.
Implicit neural representations (INR) have been recently adopted in various applications ranging from computer vision tasks to physics simulations by solving partial differential equations. Among existing INR-based works, multi-layer perceptrons with
Externí odkaz:
http://arxiv.org/abs/2410.04716
Despite the growing prevalence of artificial neural networks in real-world applications, their vulnerability to adversarial attacks remains a significant concern, which motivates us to investigate the robustness of machine learning models. While vari
Externí odkaz:
http://arxiv.org/abs/2408.09672
We study multistage distributionally robust linear optimization, where the uncertainty set is defined as a ball of distribution centered at a scenario tree using the nested distance. The resulting minimax problem is notoriously difficult to solve due
Externí odkaz:
http://arxiv.org/abs/2407.16346
Autor:
Gao, Rui, Zhu, Miaomiao
We demonstrate the existence of branched immersed 2-spheres with prescribed mean curvature, with controlled Morse index and with arbitrary codimensions in closed Riemannian manifold $N$ admitting finite fundamental group, where $\pi_k(N) \neq 0$ and
Externí odkaz:
http://arxiv.org/abs/2407.11945
Fast convolution algorithms, including Winograd and FFT, can efficiently accelerate convolution operations in deep models. However, these algorithms depend on high-precision arithmetic to maintain inference accuracy, which conflicts with the model qu
Externí odkaz:
http://arxiv.org/abs/2407.02913
In this article, we show that for a typical non-uniformly expanding unimodal map, the unique maximizing measure of a generic Lipschitz function is supported on a periodic orbit.
Comment: 18 pages
Comment: 18 pages
Externí odkaz:
http://arxiv.org/abs/2405.18083
Graph Neural Networks (GNNs) have excelled in learning from graph-structured data, especially in understanding the relationships within a single graph, i.e., intra-graph relationships. Despite their successes, GNNs are limited by neglecting the conte
Externí odkaz:
http://arxiv.org/abs/2405.03950
We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions. The distributional uncertainty sets a
Externí odkaz:
http://arxiv.org/abs/2403.14822
With increasing concerns over data privacy and model copyrights, especially in the context of collaborations between AI service providers and data owners, an innovative SG-ZSL paradigm is proposed in this work. SG-ZSL is designed to foster efficient
Externí odkaz:
http://arxiv.org/abs/2403.09363
Autor:
Tang, Shaojie, Miao, Penpen, Gao, Xingyu, Zhong, Yu, Zhu, Dantong, Wen, Haixing, Xu, Zhihui, Wei, Qiuyue, Yao, Hongping, Huang, Xin, Gao, Rui, Zhao, Chen, Zhou, Weihua
A method was proposed for the point cloud-based registration and image fusion between cardiac single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) and cardiac computed tomography angiograms (CTA). Firstly, the left ven
Externí odkaz:
http://arxiv.org/abs/2402.06841