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pro vyhledávání: '"Sardana, Nikhil"'
Large language model (LLM) scaling laws are empirical formulas that estimate changes in model quality as a result of increasing parameter count and training data. However, these formulas, including the popular Deepmind Chinchilla scaling laws, neglec
Externí odkaz:
http://arxiv.org/abs/2401.00448
Autor:
Portes, Jacob, Trott, Alex, Havens, Sam, King, Daniel, Venigalla, Abhinav, Nadeem, Moin, Sardana, Nikhil, Khudia, Daya, Frankle, Jonathan
Publikováno v:
NeurIPS 2023
Although BERT-style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past half-decade since BERT first rose to prominence, many advances have been
Externí odkaz:
http://arxiv.org/abs/2312.17482
Autor:
Sharma, Archit, Xu, Kelvin, Sardana, Nikhil, Gupta, Abhishek, Hausman, Karol, Levine, Sergey, Finn, Chelsea
Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of its resemblance to how humans and animals acquire skills through expe
Externí odkaz:
http://arxiv.org/abs/2112.09605
Autor:
Myers, Vivek, Sardana, Nikhil
Recent advances in the field of meta-learning have tackled domains consisting of large numbers of small ("few-shot") supervised learning tasks. Meta-learning algorithms must be able to rapidly adapt to any individual few-shot task, fitting to a small
Externí odkaz:
http://arxiv.org/abs/2110.11044
Autor:
Sardana, Nikhil
Publikováno v:
India Today; 6/12/2017, p57-58, 2p, 2 Color Photographs