Zobrazeno 1 - 10
of 278
pro vyhledávání: '"Yuan Jianhua"'
In this paper, we consider a class of convex programming problems with linear equality constraints, which finds broad applications in machine learning and signal processing. We propose a new adaptive balanced augmented Lagrangian (ABAL) method for so
Externí odkaz:
http://arxiv.org/abs/2410.15358
In this paper, we study the local-nonglobal minimizers of the Generalized Trust-Region subproblem $(GTR)$ and its Equality-constrained version $(GTRE)$. Firstly, the equivalence is established between the local-nonglobal minimizers of both $(GTR)$ an
Externí odkaz:
http://arxiv.org/abs/2409.01697
Autor:
Niu, Yangyang, Wei, Zhiqing, Ma, Dingyou, Yang, Xiaoyu, Wu, Huici, Feng, Zhiyong, Yuan, Jianhua
The integrated sensing and communication (ISAC) system under multi-input multi-output (MIMO) architecture achieves dual functionalities of sensing and communication on the same platform by utilizing spatial gain, which provides a feasible paradigm fa
Externí odkaz:
http://arxiv.org/abs/2407.05391
Publikováno v:
Science and Technology for Energy Transition, Vol 78, p 15 (2023)
Aiming at the problem of high fluctuation and instability of photovoltaic power, a photovoltaic power prediction method combining two techniques has been proposed in this study. In this method, the fast correlation filtering algorithm has been used t
Externí odkaz:
https://doaj.org/article/a6e55f8d173d40a7ac61ed63d42e5486
In this paper, we consider the problem of minimizing a general homogeneous quadratic function, subject to three real or four complex homogeneous quadratic inequality or equality constraints. For this problem, we present a sufficient and necessary tes
Externí odkaz:
http://arxiv.org/abs/2304.04174
Stance detection models may tend to rely on dataset bias in the text part as a shortcut and thus fail to sufficiently learn the interaction between the targets and texts. Recent debiasing methods usually treated features learned by small models or bi
Externí odkaz:
http://arxiv.org/abs/2212.10392
As aspect-level sentiment labels are expensive and labor-intensive to acquire, zero-shot aspect-level sentiment classification is proposed to learn classifiers applicable to new domains without using any annotated aspect-level data. In contrast, docu
Externí odkaz:
http://arxiv.org/abs/2209.02276
Publikováno v:
In Chinese Chemical Letters November 2024 35(11)
Publikováno v:
In Hydrometallurgy April 2024 225
Multi-domain sentiment classification deals with the scenario where labeled data exists for multiple domains but insufficient for training effective sentiment classifiers that work across domains. Thus, fully exploiting sentiment knowledge shared acr
Externí odkaz:
http://arxiv.org/abs/2104.08480