Zobrazeno 1 - 10
of 469
pro vyhledávání: '"Li, Guangxi"'
Long-tailed distributions in image recognition pose a considerable challenge due to the severe imbalance between a few dominant classes with numerous examples and many minority classes with few samples. Recently, the use of large generative models to
Externí odkaz:
http://arxiv.org/abs/2408.16273
Autor:
Zhang, Zeyu, Qi, Xuyin, Chen, Mingxi, Li, Guangxi, Pham, Ryan, Qassim, Ayub, Berry, Ella, Liao, Zhibin, Siggs, Owen, Mclaughlin, Robert, Craig, Jamie, To, Minh-Son
The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders. However, continuous monitoring of SaO2 is time-consuming and highly variable depending on patients' conditions. Rece
Externí odkaz:
http://arxiv.org/abs/2404.11525
Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors by converting an exponential number of dimensions to polynomial complexity.
Externí odkaz:
http://arxiv.org/abs/2302.09019
Publikováno v:
Advances in Neural Information Processing Systems (NeurIPS), 2022, 35: 19456-19469
Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and combinatorial optimization. When applied to tasks involving classical data, such a
Externí odkaz:
http://arxiv.org/abs/2206.08273
An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP). Although some efforts based on syntactic analysis have opened the do
Externí odkaz:
http://arxiv.org/abs/2205.05625
Publikováno v:
In Applied Thermal Engineering 15 February 2024 239
Publikováno v:
Science China Information Sciences volume 66, Article number: 129502 (2023)
Hamiltonian learning is crucial to the certification of quantum devices and quantum simulators. In this paper, we propose a hybrid quantum-classical Hamiltonian learning algorithm to find the coefficients of the Pauli operator components of the Hamil
Externí odkaz:
http://arxiv.org/abs/2103.01061
Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum
Externí odkaz:
http://arxiv.org/abs/2012.08288
Publikováno v:
Neural Networks, 2020
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e.g., image classification, natural language processing, etc. Despite the good performance, the huge number of parameters in DNNs brings challenges to
Externí odkaz:
http://arxiv.org/abs/2010.04963
Publikováno v:
Phys. Rev. Applied 16, 054035, 2021
The preparation of quantum Gibbs state is an essential part of quantum computation and has wide-ranging applications in various areas, including quantum simulation, quantum optimization, and quantum machine learning. In this paper, we propose variati
Externí odkaz:
http://arxiv.org/abs/2005.08797