Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Lou, Yifei"'
Time-varying graph signal recovery has been widely used in many applications, including climate change, environmental hazard monitoring, and epidemic studies. It is crucial to choose appropriate regularizations to describe the characteristics of the
Externí odkaz:
http://arxiv.org/abs/2405.09752
In this paper, we propose a novel approach to solving optimization problems by reformulating the optimization problem into a dynamical system, followed by the adaptive spectral Koopman (ASK) method. The Koopman operator, employed in our approach, app
Externí odkaz:
http://arxiv.org/abs/2312.14361
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
Deep neural networks have achieved significant success in the last decades, but they are not well-calibrated and often produce unreliable predictions. A large number of literature relies on uncertainty quantification to evaluate the reliability of a
Externí odkaz:
http://arxiv.org/abs/2311.05795
Quotient regularization models (QRMs) are a class of powerful regularization techniques that have gained considerable attention in recent years, due to their ability to handle complex and highly nonlinear data sets. However, the nonconvex nature of Q
Externí odkaz:
http://arxiv.org/abs/2308.04095
Poisson noise commonly occurs in images captured by photon-limited imaging systems such as in astronomy and medicine. As the distribution of Poisson noise depends on the pixel intensity value, noise levels vary from pixels to pixels. Hence, denoising
Externí odkaz:
http://arxiv.org/abs/2307.00439
Publikováno v:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5. IEEE, 2023
Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel non-convex tensor completion frameworks that are easy to implement, name
Externí odkaz:
http://arxiv.org/abs/2303.12721
In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by $k$-means clustering to segment the image. Specifical
Externí odkaz:
http://arxiv.org/abs/2301.03393
This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to accelerate optimization problems in sparse recovery. Specifically, we consider two types of minimization problems: a (single) differentiable function and the sum
Externí odkaz:
http://arxiv.org/abs/2208.12183
Publikováno v:
In International Journal of Pharmaceutics: X December 2024 8
Motivated by re-weighted $\ell_1$ approaches for sparse recovery, we propose a lifted $\ell_1$ (LL1) regularization which is a generalized form of several popular regularizations in the literature. By exploring such connections, we discover there are
Externí odkaz:
http://arxiv.org/abs/2203.05125