Zobrazeno 1 - 10
of 765
pro vyhledávání: '"Chen, YiQun"'
Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs for ranking: (1) LLMs are constrained by limited input length, precludi
Externí odkaz:
http://arxiv.org/abs/2406.11678
Autor:
Chen, Yiqun, Mao, Jiaxin, Zhang, Yi, Ma, Dehong, Xia, Long, Fan, Jun, Shi, Daiting, Cheng, Zhicong, Gu, Simiu, Yin, Dawei
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible. Existing methods primarily utilize a paradigm of "greedy selection", i.e., selecting one document with the highes
Externí odkaz:
http://arxiv.org/abs/2403.17421
Autor:
Liang, Weixin, Rajani, Nazneen, Yang, Xinyu, Ozoani, Ezinwanne, Wu, Eric, Chen, Yiqun, Smith, Daniel Scott, Zou, James
The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in various applications. Although developers are encouraged to produce model
Externí odkaz:
http://arxiv.org/abs/2402.05160
Autor:
Mao, Hangyu, Zhao, Rui, Li, Ziyue, Xu, Zhiwei, Chen, Hao, Chen, Yiqun, Zhang, Bin, Xiao, Zhen, Zhang, Junge, Yin, Jiangjin
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work studies the former. Specifically, the Perception and Decision-making Interleaving Transformer (PDiT) network is proposed, which
Externí odkaz:
http://arxiv.org/abs/2312.15863
We present a conceptually simple, efficient, and general framework for localization problems in DETR-like models. We add plugins to well-trained models instead of inefficiently designing new models and training them from scratch. The method, called R
Externí odkaz:
http://arxiv.org/abs/2307.11828
Autor:
Chen, Yiqun, Zou, James
Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DA
Externí odkaz:
http://arxiv.org/abs/2306.08310
Autor:
Overton, Christopher E., Abbey, Rachel, Baird, Tarrion, Christie, Rachel, Daniel, Owen, Day, Julie, Gittins, Matthew, Jones, Owen, Paton, Robert, Tang, Maria, Ward, Tom, Wilkinson, Jack, Woodrow-Hill, Camilla, Aldridge, Tim, Chen, Yiqun
Objectives: To identify and quantify risk factors that contribute to clusters of COVID-19 in the workplace. Methods: We identified clusters of COVID-19 cases in the workplace and investigated the characteristics of the individuals, the workplaces, th
Externí odkaz:
http://arxiv.org/abs/2305.08745
Autor:
Mao, Hangyu, Zhao, Rui, Chen, Hao, Hao, Jianye, Chen, Yiqun, Li, Dong, Zhang, Junge, Xiao, Zhen
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods com
Externí odkaz:
http://arxiv.org/abs/2212.14538
Fully convolutional detectors discard the one-to-many assignment and adopt a one-to-one assigning strategy to achieve end-to-end detection but suffer from the slow convergence issue. In this paper, we revisit these two assignment methods and find tha
Externí odkaz:
http://arxiv.org/abs/2211.13859
Autor:
Chen, Yiqun, Mao, Hangyu, Mao, Jiaxin, Wu, Shiguang, Zhang, Tianle, Zhang, Bin, Yang, Wei, Chang, Hongxing
Centralized Training with Decentralized Execution (CTDE) has emerged as a widely adopted paradigm in multi-agent reinforcement learning, emphasizing the utilization of global information for learning an enhanced joint $Q$-function or centralized crit
Externí odkaz:
http://arxiv.org/abs/2210.08872