Zobrazeno 1 - 10
of 1 702
pro vyhledávání: '"P Maheswaran"'
Autor:
Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Ramisa, Arnau, Vidal, René, Sathiamoorthy, Maheswaran, Kasirzadeh, Atoosa, Milano, Silvia
Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, te
Externí odkaz:
http://arxiv.org/abs/2404.00579
Autor:
Cao, Yuwei, Mehta, Nikhil, Yi, Xinyang, Keshavan, Raghunandan, Heldt, Lukasz, Hong, Lichan, Chi, Ed H., Sathiamoorthy, Maheswaran
Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like retrieval. We attribute this to a mismatch between LLMs' knowledge and
Externí odkaz:
http://arxiv.org/abs/2404.00245
Autor:
Singh, Anima, Vu, Trung, Mehta, Nikhil, Keshavan, Raghunandan, Sathiamoorthy, Maheswaran, Zheng, Yilin, Hong, Lichan, Heldt, Lukasz, Wei, Li, Tandon, Devansh, Chi, Ed H., Yi, Xinyang
Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, causing problems of learning unseen and long-tail items, especially when i
Externí odkaz:
http://arxiv.org/abs/2306.08121
Autor:
Kang, Wang-Cheng, Ni, Jianmo, Mehta, Nikhil, Sathiamoorthy, Maheswaran, Hong, Lichan, Chi, Ed, Cheng, Derek Zhiyuan
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emer
Externí odkaz:
http://arxiv.org/abs/2305.06474
Autor:
Rajput, Shashank, Mehta, Nikhil, Singh, Anima, Keshavan, Raghunandan H., Vu, Trung, Heldt, Lukasz, Hong, Lichan, Tay, Yi, Tran, Vinh Q., Samost, Jonah, Kula, Maciej, Chi, Ed H., Sathiamoorthy, Maheswaran
Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2305.05065
Autor:
Veera Kumar Maheswaran, Yuichi Otsuka, James A. Baskaradas, Venkata Ratnam Devanaboyina, Sriram Subramanian, Atsuki Shinbori, Takuya Sori, Michi Nishioka, Septi Perwitasari
Publikováno v:
Earth, Planets and Space, Vol 76, Iss 1, Pp 1-9 (2024)
Abstract To investigate solar activity dependence of the coupling between medium-scale traveling ionosphere disturbance (MSTID) and sporadic E (Es) layer, we analyzed the total electron content (TEC) obtained from a Japanese global positioning system
Externí odkaz:
https://doaj.org/article/5eca7db2494a4036b22b4cfb80506b29
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-6 (2024)
Abstract This study aimed to register and analyse outcomes after iatrogenic ureteral injuries (IUI) with special emphasis on potential consequences of a delayed diagnosis, and further to analyse if the incidence of IUI has changed during the study pe
Externí odkaz:
https://doaj.org/article/edcc5548f0894cc9beb03d863b66d851
Autor:
Tang, Jiaxi, Drori, Yoel, Chang, Daryl, Sathiamoorthy, Maheswaran, Gilmer, Justin, Wei, Li, Yi, Xinyang, Hong, Lichan, Chi, Ed H.
Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we found that research on stabilizing the training for such models is severel
Externí odkaz:
http://arxiv.org/abs/2302.09178
Autor:
S. Lakshmi, C. P. Maheswaran
Publikováno v:
Automatika, Vol 65, Iss 2, Pp 425-440 (2024)
The prediction of final semester grades is a crucial undertaking in education, offering insights into student performance and enabling timely interventions to support their academic journey. This paper employs a deep learning approach, specifically g
Externí odkaz:
https://doaj.org/article/b22c52f5599c47aa8ff525f1f1038f00
Autor:
Anand Raj Dhanapal, Muthu Thiruvengadam, Jayavarshini Vairavanathan, Baskar Venkidasamy, Maheswaran Easwaran, Mansour Ghorbanpour
Publikováno v:
ACS Omega, Vol 9, Iss 12, Pp 13522-13533 (2024)
Externí odkaz:
https://doaj.org/article/989f438784244e6886e96ccf234ff6d5