A Quantum Annealing Instance Selection Approach for Efficient and Effective Transformer Fine-Tuning

Author:

Pasin Andrea1ORCID,Cunha Washington2ORCID,Gonçalves Marcos André2ORCID,Ferro Nicola1ORCID

Affiliation:

1. Università degli Studi di Padova, Padova, Italy

2. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Funder

Amazon Web Services

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de São Paulo

Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior

The Research Support Foundation of the State of Minas Gerais

Publisher

ACM

Reference44 articles.

1. Quantum Algorithm Implementations for Beginners;Abhijith J.;ACM Transactions on Quantum Computing (TQC),2022

2. An information-theoretic perspective of tf–idf measures

3. An automated approach to aspect-based sentiment analysis of apps reviews using machine and deep learning

4. Mohammad H Amin Andrew D King Jack Raymond Richard Harris William Bernoudy Andrew J Berkley Kelly Boothby Anatoly Smirnov Fabio Altomare Michael Babcock et al. 2023. Quantum error mitigation in quantum annealing. arXiv preprint arXiv:2311.01306 (2023).

5. How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey

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