Hayata Yamasaki — accepted AI-conference papers

2023

Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation

Hayata Yamasaki, Sathyawageeswar Subramanian, Satoshi Hayakawa, Sho Sonoda

ICML 2023poster

A significant challenge in the field of quantum machine learning (QML) is to establish applications of quantum computation to accelerate common tasks in machine learning such as those for neural networks. Ridgelet transform has been a fundamental mathematical tool in the theoretical studies of neura…

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2020

Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions

Hayata Yamasaki, Sathyawageeswar Subramanian, Sho Sonoda, Masato Koashi

NeurIPS 2020poster

Kernel methods augmented with random features give scalable algorithms for learning from big data. But it has been computationally hard to sample random features according to a probability distribution that is optimized for the data, so as to minimize the required number of features for achieving th…

Cited by 22 Source ↗PDF ↗Scholar ↗