Zobrazeno 1 - 10
of 575
pro vyhledávání: '"Liang Jiaming"'
This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functional and stationary complexity bou
Externí odkaz:
http://arxiv.org/abs/2407.10073
Autor:
Liang, Jiaming, Lei, Chuan, Qin, Xiao, Zhang, Jiani, Katsifodimos, Asterios, Faloutsos, Christos, Rangwala, Huzefa
Data-centric AI focuses on understanding and utilizing high-quality, relevant data in training machine learning (ML) models, thereby increasing the likelihood of producing accurate and useful results. Automatic feature augmentation, aiming to augment
Externí odkaz:
http://arxiv.org/abs/2406.09534
Autor:
He Yongqi, Zhao Jia, Feng Defeng, Huang Zhibo, Liang Jiaming, Zheng Yufei, Cheng Jinping, Ying Jifeng, Wang Zhoufei
Publikováno v:
Rice Science, Vol 27, Iss 4, Pp 302-314 (2020)
A number of internal signals are required for seed germination. However, the precise signalling responses in the initial imbibition of seed germination are not yet fully understood in rice. In this study, the RNA sequencing (RNA-Seq) approach was con
Externí odkaz:
https://doaj.org/article/89a1b460aa984002bed49f087011cbc8
Autor:
Liang, Jiaming, Chen, Yongxin
We consider convex optimization with non-smooth objective function and log-concave sampling with non-smooth potential (negative log density). In particular, we study two specific settings where the convex objective/potential function is either semi-s
Externí odkaz:
http://arxiv.org/abs/2404.02239
Temporal action detection (TAD) aims to locate action positions and recognize action categories in long-term untrimmed videos. Although many methods have achieved promising results, their robustness has not been thoroughly studied. In practice, we ob
Externí odkaz:
http://arxiv.org/abs/2403.20254
We study the rate at which the initial and current random variables become independent along a Markov chain, focusing on the Langevin diffusion in continuous time and the Unadjusted Langevin Algorithm (ULA) in discrete time. We measure the dependence
Externí odkaz:
http://arxiv.org/abs/2402.17067
Autor:
Liang, Jiaming
This paper proposes a stochastic proximal point method to solve a stochastic convex composite optimization problem. High probability results in stochastic optimization typically hinge on restrictive assumptions on the stochastic gradient noise, for e
Externí odkaz:
http://arxiv.org/abs/2402.08992
This paper presents a Bayesian framework for inferring the posterior of the extended state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or final destination. The methodology is thus for joint tracki
Externí odkaz:
http://arxiv.org/abs/2311.06139
Autor:
Bi, Xiuli, Liang, Jiaming
In existing splicing forgery datasets, the insufficient semantic varieties of spliced regions cause a problem that trained detection models overfit semantic features rather than splicing traces. Meanwhile, because of the absence of a reasonable datas
Externí odkaz:
http://arxiv.org/abs/2310.10070
We consider the sampling problem from a composite distribution whose potential (negative log density) is $\sum_{i=1}^n f_i(x_i)+\sum_{j=1}^m g_j(y_j)+\sum_{i=1}^n\sum_{j=1}^m\frac{\sigma_{ij}}{2\eta} \Vert x_i-y_j \Vert^2_2$ where each of $x_i$ and $
Externí odkaz:
http://arxiv.org/abs/2306.13801