3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Sparse Upcycling: Inference Inefficient Finetuning
Sasha Doubov, Nikhil Sardana, Vitaliy Chiley
Small, highly trained, open-source large language models are widely used due to their inference efficiency, but further improving their quality remains a challenge. Sparse upcyclin…
cs.LG2021★ 3 cited
Autonomous Reinforcement Learning: Formalism and Benchmarking
Archit Sharma, Kelvin Xu, Nikhil Sardana +4
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…
cs.LG2021
Bayesian Meta-Learning Through Variational Gaussian Processes
Vivek Myers, Nikhil Sardana
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 abl…