Zobrazeno 1 - 10
of 741
pro vyhledávání: '"Yang Yibo"'
Online continual learning (OCL) seeks to learn new tasks from data streams that appear only once, while retaining knowledge of previously learned tasks. Most existing methods rely on replay, focusing on enhancing memory retention through regularizati
Externí odkaz:
http://arxiv.org/abs/2412.18177
Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention of stress-related disorders. The current early detection models perform "black box" inference sufferin
Externí odkaz:
http://arxiv.org/abs/2412.14009
Diffusion probabilistic models have achieved mainstream success in many generative modeling tasks, from image generation to inverse problem solving. A distinct feature of these models is that they correspond to deep hierarchical latent variable model
Externí odkaz:
http://arxiv.org/abs/2412.10935
Autor:
Han, Xue-Ying, Hua, Jun, Ji, Xiangdong, Lü, Cai-Dian, Schäfer, Andreas, Su, Yushan, Wang, Wei, Xu, Ji, Yang, Yibo, Zhang, Jian-Hui, Zhang, Qi-An, Zhao, Shuai
We develop an approach for calculating heavy quark effective theory (HQET) light-cone distribution amplitudes (LCDAs) by employing a sequential effective theory methodology. The theoretical foundation of the framework is established, elucidating how
Externí odkaz:
http://arxiv.org/abs/2410.18654
Few-shot class-incremental learning (FSCIL) confronts the challenge of integrating new classes into a model with minimal training samples while preserving the knowledge of previously learned classes. Traditional methods widely adopt static adaptation
Externí odkaz:
http://arxiv.org/abs/2407.06136
Autor:
Yang, Yibo, Li, Xiaojie, Alfarra, Motasem, Hammoud, Hasan, Bibi, Adel, Torr, Philip, Ghanem, Bernard
Relieving the reliance of neural network training on a global back-propagation (BP) has emerged as a notable research topic due to the biological implausibility and huge memory consumption caused by BP. Among the existing solutions, local learning op
Externí odkaz:
http://arxiv.org/abs/2406.05222
Autor:
Yang, Yibo, Li, Xiaojie, Zhou, Zhongzhu, Song, Shuaiwen Leon, Wu, Jianlong, Nie, Liqiang, Ghanem, Bernard
Current parameter-efficient fine-tuning (PEFT) methods build adapters widely agnostic of the context of downstream task to learn, or the context of important knowledge to maintain. As a result, there is often a performance gap compared to full-parame
Externí odkaz:
http://arxiv.org/abs/2406.05223
Autor:
WANG Yongjian, LIU Fucai, YANG Yibo, XIE Xiangming, YAO Chukang, XIAO Min, GUO Wenying, WANG Hengchang
Publikováno v:
Renmin Zhujiang, Vol 41 (2020)
Because ultra-high performance concrete (UHPC) has ultra-high strength and ultra-highdurability, it is a possible way for covering the outer surface of the reinforced concretestructure with UHPC permanent form to effectively improve the service life
Externí odkaz:
https://doaj.org/article/f2f6b09923d042a4909da7f952b9f557
Self-supervised learning has achieved remarkable success in acquiring high-quality representations from unlabeled data. The widely adopted contrastive learning framework aims to learn invariant representations by minimizing the distance between posit
Externí odkaz:
http://arxiv.org/abs/2403.12003