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pro vyhledávání: '"Guan, Lin"'
Autor:
Si, Zihua, Guan, Lin, Sun, ZhongXiang, Zang, Xiaoxue, Lu, Jing, Hui, Yiqun, Cao, Xingchao, Yang, Zeyu, Zheng, Yichen, Leng, Dewei, Zheng, Kai, Zhang, Chenbin, Niu, Yanan, Song, Yang, Gai, Kun
The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practitioners. Existing work, such as SIM and TWIN, typically employs a two-
Externí odkaz:
http://arxiv.org/abs/2407.16357
Autor:
Gundawar, Atharva, Verma, Mudit, Guan, Lin, Valmeekam, Karthik, Bhambri, Siddhant, Kambhampati, Subbarao
As the applicability of Large Language Models (LLMs) extends beyond traditional text processing tasks, there is a burgeoning interest in their potential to excel in planning and reasoning assignments, realms traditionally reserved for System 2 cognit
Externí odkaz:
http://arxiv.org/abs/2405.20625
This paper addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a Real-Time Compressed Sensing (RTC
Externí odkaz:
http://arxiv.org/abs/2404.15781
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences. Their full power is better harnessed when the models are coupled with external verifiers
Externí odkaz:
http://arxiv.org/abs/2402.04210
Autor:
Kambhampati, Subbarao, Valmeekam, Karthik, Guan, Lin, Verma, Mudit, Stechly, Kaya, Bhambri, Siddhant, Saldyt, Lucas, Murthy, Anil
Publikováno v:
Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024
There is considerable confusion about the role of Large Language Models (LLMs) in planning and reasoning tasks. On one side are over-optimistic claims that LLMs can indeed do these tasks with just the right prompting or self-verification strategies.
Externí odkaz:
http://arxiv.org/abs/2402.01817
There is a growing interest in applying pre-trained large language models (LLMs) to planning problems. However, methods that use LLMs directly as planners are currently impractical due to several factors, including limited correctness of plans, stron
Externí odkaz:
http://arxiv.org/abs/2305.14909
Autor:
Chang, Jianxin, Zhang, Chenbin, Fu, Zhiyi, Zang, Xiaoxue, Guan, Lin, Lu, Jing, Hui, Yiqun, Leng, Dewei, Niu, Yanan, Song, Yang, Gai, Kun
Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a
Externí odkaz:
http://arxiv.org/abs/2302.02352
Generating complex behaviors that satisfy the preferences of non-expert users is a crucial requirement for AI agents. Interactive reward learning from trajectory comparisons (a.k.a. RLHF) is one way to allow non-expert users to convey complex objecti
Externí odkaz:
http://arxiv.org/abs/2210.15906
Autor:
Soni, Utkarsh, Thakur, Nupur, Sreedharan, Sarath, Guan, Lin, Verma, Mudit, Marquez, Matthew, Kambhampati, Subbarao
There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected to behave in a manner that is preferred by the human. This requires the
Externí odkaz:
http://arxiv.org/abs/2210.15096
Publikováno v:
BMC Musculoskeletal Disorders, Vol 25, Iss 1, Pp 1-13 (2024)
Abstract Background Shoulder disorders, particularly rotator cuff tears, are prevalent musculoskeletal conditions related to aging. Although the widely used suture anchor technique provides strong mechanical support to the tendon, it is associated wi
Externí odkaz:
https://doaj.org/article/c9a3f2a994db42f7aaa584484941f9d0