Zobrazeno 1 - 10
of 537
pro vyhledávání: '"Zhang, Xiaoqun"'
Recently, medical image synthesis gains more and more popularity, along with the rapid development of generative models. Medical image synthesis aims to generate an unacquired image modality, often from other observed data modalities. Synthesized ima
Externí odkaz:
http://arxiv.org/abs/2409.03977
Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the availability of big datasets. One effective strategy to mitigate the issue of insufficient data is to firs
Externí odkaz:
http://arxiv.org/abs/2409.02708
Autor:
Cao, Xiang, Zhang, Xiaoqun
Diffusion models have been widely studied as effective generative tools for solving inverse problems. The main ideas focus on performing the reverse sampling process conditioned on noisy measurements, using well-established numerical solvers for grad
Externí odkaz:
http://arxiv.org/abs/2408.17333
Graph neural networks (GNNs) have become pivotal tools for processing graph-structured data, leveraging the message passing scheme as their core mechanism. However, traditional GNNs often grapple with issues such as instability, over-smoothing, and o
Externí odkaz:
http://arxiv.org/abs/2407.06988
Large Language Models (LLMs) have achieved significant progress across various fields and have exhibited strong potential in evolutionary computation, such as generating new solutions and automating algorithm design. Surrogate-assisted selection is a
Externí odkaz:
http://arxiv.org/abs/2406.10675
Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive expensive problems. Utilizing surrogate models to substitute the optimization function can significantly reduce reliance on the function evaluations during
Externí odkaz:
http://arxiv.org/abs/2405.16494
The regularized D-bar method is one of the most prominent methods for solving Electrical Impedance Tomography (EIT) problems due to its efficiency and simplicity. It provides a direct approach by applying low-pass filtering to the scattering data in
Externí odkaz:
http://arxiv.org/abs/2407.03335
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This often leads to significant computational resources being consumed for
Externí odkaz:
http://arxiv.org/abs/2403.14413
This paper focuses on recovering a low-rank tensor from its incomplete measurements. We propose a novel algorithm termed the Single Mode Quasi Riemannian Gradient Descent (SM-QRGD). By exploiting the benefits of both fixed-rank matrix tangent space p
Externí odkaz:
http://arxiv.org/abs/2401.15925
Publikováno v:
CSIAM Transaction on Applied Mathematics, 5 (2024), pp. 615-635
Massive multiple-input multiple-output (MIMO) systems employ a large number of antennas to achieve gains in capacity, spectral efficiency, and energy efficiency. However, the large antenna array also incurs substantial storage and computational costs
Externí odkaz:
http://arxiv.org/abs/2401.09792