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Computer Science > Machine Learning

arXiv:2205.11833 (cs)
[Submitted on 24 May 2022]

Title:Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Model

Authors:Sosuke Kobayashi, Shun Kiyono, Jun Suzuki, Kentaro Inui
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Abstract:Ensembling is a popular method used to improve performance as a last resort. However, ensembling multiple models finetuned from a single pretrained model has been not very effective; this could be due to the lack of diversity among ensemble members. This paper proposes Multi-Ticket Ensemble, which finetunes different subnetworks of a single pretrained model and ensembles them. We empirically demonstrated that winning-ticket subnetworks produced more diverse predictions than dense networks, and their ensemble outperformed the standard ensemble on some tasks.
Comments: Workshop on Challenges & Perspectives in Creating Large Language Models (BigScience) 2022
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2205.11833 [cs.LG]
  (or arXiv:2205.11833v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2205.11833

Submission history

From: Sosuke Kobayashi [view email]
[v1] Tue, 24 May 2022 06:54:33 UTC (2,189 KB)
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