Zobrazeno 1 - 10
of 2 422
pro vyhledávání: '"wang, Chenyang"'
Existing face super-resolution (FSR) methods have made significant advancements, but they primarily super-resolve face with limited visual information, original pixel-wise space in particular, commonly overlooking the pluralistic clues, like the high
Externí odkaz:
http://arxiv.org/abs/2411.09293
Autor:
Wang, Haoyu, Qiang, Chunyu, Wang, Tianrui, Gong, Cheng, Liu, Qiuyu, Jiang, Yu, Wang, Xiaobao, Wang, Chenyang, Zhang, Chen
Recent advancements in speech synthesis models, trained on extensive datasets, have demonstrated remarkable zero-shot capabilities. These models can control content, timbre, and emotion in generated speech based on prompt inputs. Despite these advanc
Externí odkaz:
http://arxiv.org/abs/2409.18512
Autor:
Tang, Yuanjiang, Wang, Chenyang, Liu, Bei, Peng, Jin, Liang, Chao, Li, Yaohua, Zhao, Xian, Lu, Cuicui, Zhang, Shuang, Liu, Yong-Chun
Spontaneous symmetry breaking plays a pivotal role in physics ranging from the emergence of elementary particles to the phase transitions of matter. The spontaneous breaking of continuous time translation symmetry leads to a novel state of matter nam
Externí odkaz:
http://arxiv.org/abs/2407.07697
Persuasive social robots employ their social influence to modulate children's behaviours in child-robot interaction. In this work, we introduce the Child-Robot Relational Norm Intervention (CRNI) model, leveraging the passive role of social robots an
Externí odkaz:
http://arxiv.org/abs/2406.07721
Autor:
Wang, Chenyang, Yang, Yun
This work introduces a new method for selecting the number of components in finite mixture models (FMMs) using variational Bayes, inspired by the large-sample properties of the Evidence Lower Bound (ELBO) derived from mean-field (MF) variational appr
Externí odkaz:
http://arxiv.org/abs/2404.16746
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, where old data from experienced tasks is unavailable when learning from a new task. To mitigate the problem, a line of methods propose to replay the da
Externí odkaz:
http://arxiv.org/abs/2401.06548
Autor:
Yang, Shenghao, Wang, Chenyang, Liu, Yankai, Xu, Kangping, Ma, Weizhi, Liu, Yiqun, Zhang, Min, Zeng, Haitao, Feng, Junlan, Deng, Chao
Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually utilize ID embeddings to represent items, which are not shared across di
Externí odkaz:
http://arxiv.org/abs/2311.10501
In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints robustification, feasibility, and closed-loop performance. To this end, w
Externí odkaz:
http://arxiv.org/abs/2311.04303
Employing Stochastic Nonlinear Model Predictive Control (SNMPC) for real-time applications is challenging due to the complex task of propagating uncertainties through nonlinear systems. This difficulty becomes more pronounced in high-dimensional syst
Externí odkaz:
http://arxiv.org/abs/2310.18753