Zobrazeno 1 - 10
of 226
pro vyhledávání: '"Liang Yuchen"'
Autor:
Zhang Huiying, Chu Mingfeng, Cheng Wei, Chen Shuiyun, Liang Yuchen, Wang Honghai, Chen Xuelong, Qi Yanping
Publikováno v:
Frontiers in Cellular and Infection Microbiology, Vol 14 (2024)
BackgroundBovine paratuberculosis is a chronic infectious disease of ruminants primarily caused by Mycobacterium avium subsp. paratuberculosis (MAP). It is essentially a chronic granulomatous enteritis characterized by intractable diarrhea, progressi
Externí odkaz:
https://doaj.org/article/7b40d0965c1c45cdb646e070915c0d09
The denoising diffusion model has recently emerged as a powerful generative technique, capable of transforming noise into meaningful data. While theoretical convergence guarantees for diffusion models are well established when the target distribution
Externí odkaz:
http://arxiv.org/abs/2410.13746
The curvature of ODE trajectories in diffusion models hinders their ability to generate high-quality images in a few number of function evaluations (NFE). In this paper, we propose a novel and effective approach to reduce trajectory curvature by util
Externí odkaz:
http://arxiv.org/abs/2409.17487
Accelerated diffusion models hold the potential to significantly enhance the efficiency of standard diffusion processes. Theoretically, these models have been shown to achieve faster convergence rates than the standard $\mathcal O(1/\epsilon^2)$ rate
Externí odkaz:
http://arxiv.org/abs/2402.13901
Radar offers the advantage of providing additional physical properties related to observed objects. In this study, we design a physical-enhanced radar-inertial odometry system that capitalizes on the Doppler velocities and radar cross-section informa
Externí odkaz:
http://arxiv.org/abs/2402.02200
The problem of quickest detection of a change in the distribution of a sequence of independent observations is considered. It is assumed that the pre-change distribution is known (accurately estimated), while the only information about the post-chang
Externí odkaz:
http://arxiv.org/abs/2309.16171
Autor:
Hoover, Benjamin, Liang, Yuchen, Pham, Bao, Panda, Rameswar, Strobelt, Hendrik, Chau, Duen Horng, Zaki, Mohammed J., Krotov, Dmitry
Publikováno v:
37th Conference on Neural Information Processing Systems (NeurIPS 2023)
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. Attention is the power-house driving modern deep learning successes, but it lacks clear theoretical
Externí odkaz:
http://arxiv.org/abs/2302.07253
The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is completely unknown. A window-limited leave-one-out (LOO) CuSum
Externí odkaz:
http://arxiv.org/abs/2211.00223
The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either direc
Externí odkaz:
http://arxiv.org/abs/2208.14376
Publikováno v:
In Journal of Cleaner Production 20 October 2024 477