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pro vyhledávání: '"Pimpalkhute, Varad"'
We investigate the integration of Large Language Models (LLMs) into query encoders to improve dense retrieval without increasing latency and cost, by circumventing the dependency on LLMs at inference time. SoftQE incorporates knowledge from LLMs by m
Externí odkaz:
http://arxiv.org/abs/2402.12663
Meta Learning has been in focus in recent years due to the meta-learner model's ability to adapt well and generalize to new tasks, thus, reducing both the time and data requirements for learning. However, a major drawback of meta learner is that, to
Externí odkaz:
http://arxiv.org/abs/2110.14459
Autor:
Alappat, Agnel Lazar, Nakhate, Prajwal, Suman, Sagar, Chandurkar, Ambarish, Pimpalkhute, Varad, Jain, Tapan
Much of the recent research work in image retrieval, has been focused around using Neural Networks as the core component. Many of the papers in other domain have shown that training multiple models, and then combining their outcomes, provide good res
Externí odkaz:
http://arxiv.org/abs/2110.14455
Autor:
Dhok, Shivani, Pimpalkhute, Varad, Chandurkar, Ambarish, Bhurane, Ankit A., Sharma, Manish, Acharya, U. Rajendra
Publikováno v:
In Computers in Biology and Medicine April 2020 119
Autor:
Pimpalkhute, Varad A., Page, Rutvik, Kothari, Ashwin, Bhurchandi, Kishor M., Kamble, Vipin Milind
Publikováno v:
IEEE Transactions on Image Processing; 2021, Vol. 30, p1962-1972, 11p
Autor:
Nazli Goharian, Nicola Tonellotto, Yulan He, Aldo Lipani, Graham McDonald, Craig Macdonald, Iadh Ounis
The six-volume set LNCS 14608, 14609, 14609, 14610, 14611, 14612 and 14613 constitutes the refereed proceedings of the 46th European Conference on IR Research, ECIR 2024, held in Glasgow, UK, during March 24–28, 2024.The 57 full papers, 18 finding