Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Zhong, Han"'
Autor:
Deng, Xun, Liu, Junlong, Zhong, Han, Feng, Fuli, Shen, Chen, He, Xiangnan, Ye, Jieping, Wang, Zheng
Active clustering aims to boost the clustering performance by integrating human-annotated pairwise constraints through strategic querying. Conventional approaches with semi-supervised clustering schemes encounter high query costs when applied to larg
Externí odkaz:
http://arxiv.org/abs/2407.10196
Autor:
Fu, Rong, Su, Zhongling, Zhong, Han-Sen, Zhao, Xiti, Zhang, Jianyang, Pan, Feng, Zhang, Pan, Zhao, Xianhe, Chen, Ming-Cheng, Lu, Chao-Yang, Pan, Jian-Wei, Pei, Zhiling, Zhang, Xingcheng, Ouyang, Wanli
Quantum Computational Superiority boasts rapid computation and high energy efficiency. Despite recent advances in classical algorithms aimed at refuting the milestone claim of Google's sycamore, challenges remain in generating uncorrelated samples of
Externí odkaz:
http://arxiv.org/abs/2407.00769
Autor:
Zhao, Xian-He, Zhong, Han-Sen, Pan, Feng, Chen, Zi-Han, Fu, Rong, Su, Zhongling, Xie, Xiaotong, Zhao, Chaoxing, Zhang, Pan, Ouyang, Wanli, Lu, Chao-Yang, Pan, Jian-Wei, Chen, Ming-Cheng
Random quantum circuit sampling serves as a benchmark to demonstrate quantum computational advantage. Recent progress in classical algorithms, especially those based on tensor network methods, has significantly reduced the classical simulation time a
Externí odkaz:
http://arxiv.org/abs/2406.18889
Integrating 2D Magnets for Quantum Devices: from Materials and Characterization to Future Technology
The unveiling of 2D van der Waals magnetism in 2017 ignited a surge of interest in low-dimensional magnetism. With dimensions reduced, research has delved into facile electric control of 2D magnetism, high-quality heterostructure design, and new devi
Externí odkaz:
http://arxiv.org/abs/2406.12136
Autor:
Liu, Jie, Zhou, Zhanhui, Liu, Jiaheng, Bu, Xingyuan, Yang, Chao, Zhong, Han-Sen, Ouyang, Wanli
Direct Preference Optimization (DPO), a standard method for aligning language models with human preferences, is traditionally applied to offline preferences. Recent studies show that DPO benefits from iterative training with online preferences labele
Externí odkaz:
http://arxiv.org/abs/2406.11817
Autor:
Liu, Xutong, Wang, Siwei, Zuo, Jinhang, Zhong, Han, Wang, Xuchuang, Wang, Zhiyong, Li, Shuai, Hajiesmaili, Mohammad, Lui, John C. S., Chen, Wei
We introduce a novel framework of combinatorial multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMAB-MT), where the outcome of each arm is a $d$-dimensional multivariant random variable and the feedback follows a g
Externí odkaz:
http://arxiv.org/abs/2406.01386
We systematically investigate quantum algorithms and lower bounds for mean estimation given query access to non-identically distributed samples. On the one hand, we give quantum mean estimators with quadratic quantum speed-up given samples from diffe
Externí odkaz:
http://arxiv.org/abs/2405.12838
Autor:
Wei, Gengchen, Pang, Xinle, Zhang, Tianning, Sun, Yu, Qian, Xun, Lin, Chen, Zhong, Han-Sen, Ouyang, Wanli
With over 200 million published academic documents and millions of new documents being written each year, academic researchers face the challenge of searching for information within this vast corpus. However, existing retrieval systems struggle to un
Externí odkaz:
http://arxiv.org/abs/2405.11461
Autor:
Zhong, Han, Feng, Guhao, Xiong, Wei, Cheng, Xinle, Zhao, Li, He, Di, Bian, Jiang, Wang, Liwei
In the classical Reinforcement Learning from Human Feedback (RLHF) framework, Proximal Policy Optimization (PPO) is employed to learn from sparse, sentence-level rewards -- a challenging scenario in traditional deep reinforcement learning. Despite th
Externí odkaz:
http://arxiv.org/abs/2404.18922
We study infinite-horizon average-reward Markov decision processes (AMDPs) in the context of general function approximation. Specifically, we propose a novel algorithmic framework named Local-fitted Optimization with OPtimism (LOOP), which incorporat
Externí odkaz:
http://arxiv.org/abs/2404.12648