no code implementations • 25 Jan 2024 • Hongyu Lu, Patrick P. Mercier
This paper introduces a conversion matrix method for linear periodically time-variant (LPTV) digital phase-locked loop (DPLL) phase noise modeling that offers precise and computationally efficient results to enable rapid design iteration and optimization.
no code implementations • 11 Dec 2023 • Ruijie Hou, Zhaoyang Yang, Yu Ming, Hongyu Lu, Zhuobin Zheng, Yu Chen, Qinsong Zeng, Ming Chen
Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms.
no code implementations • 19 Nov 2023 • Gaowei Zhang, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ji-Rong Wen
We find that scaling up the model size can greatly boost the performance on these challenging tasks, which again verifies the benefits of large recommendation models.
1 code implementation • 15 Nov 2023 • Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ming Chen, Ji-Rong Wen
To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems.
no code implementations • 14 Jul 2023 • Qi-Wei Wang, Hongyu Lu, Yu Chen, Da-Wei Zhou, De-Chuan Zhan, Ming Chen, Han-Jia Ye
The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications.
1 code implementation • 15 May 2023 • Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, Wayne Xin Zhao
Recently, large language models (LLMs) (e. g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.
no code implementations • 25 Dec 2019 • Rohan Mahadev, Hongyu Lu
Our brains combine vision and hearing to create a more elaborate interpretation of the world.