Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Xie, Xinheng"'
Understanding adversarial examples is crucial for improving the model's robustness, as they introduce imperceptible perturbations that deceive models. Effective adversarial examples, therefore, offer the potential to train more robust models by remov
Externí odkaz:
http://arxiv.org/abs/2412.03539
Autor:
Xie, Xinheng, Yamaguchi, Kureha, Leblanc, Margaux, Malzard, Simon, Chhabra, Varun, Nockles, Victoria, Wu, Yue
The rapid advancement of machine learning technologies raises questions about the security of machine learning models, with respect to both training-time (poisoning) and test-time (evasion, impersonation, and inversion) attacks. Models performing ima
Externí odkaz:
http://arxiv.org/abs/2409.04982
In this paper we investigate the existence, uniqueness and approximation of solutions of delay differential equations (DDEs) with the right-hand side functions $f=f(t,x,z)$ that are Lipschitz continuous with respect to $x$ but only H\"older continuou
Externí odkaz:
http://arxiv.org/abs/2401.11658
On approximation of solutions of stochastic delay differential equations via randomized Euler scheme
We investigate existence, uniqueness and approximation of solutions to stochastic delay differential equations (SDDEs) under Carath\'eodory-type drift coefficients. Moreover, we also assume that both drift $f=f(t,x,z)$ and diffusion $g=g(t,x,z)$ coef
Externí odkaz:
http://arxiv.org/abs/2306.08926
Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neu
Externí odkaz:
http://arxiv.org/abs/2305.11049
Publikováno v:
In Pattern Recognition April 2024 148
On approximation of solutions of stochastic delay differential equations via randomized Euler scheme
Publikováno v:
In Applied Numerical Mathematics March 2024 197:143-163