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Found 12 papers, 5 papers with code
JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders.
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Interestingly, although these two types of networks originate from different observations, they are inherently linked through the common multilinearity structure underlying both TNs and NNs, thereby motivating a significant number of intellectual developments regarding combinations of TNs and NNs.
Concentration of Data Encoding in Parameterized Quantum Circuits
This result in particular implies that the average encoded state will concentrate on the maximally mixed state at an exponential speed on depth.
Quantum Self-Attention Neural Networks for Text Classification
An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP).
A Hybrid Quantum-Classical Hamiltonian Learning Algorithm
Hamiltonian learning is crucial to the certification of quantum devices and quantum simulators.
VSQL: Variational Shadow Quantum Learning for Classification
Classification of quantum data is essential for quantum machine learning and near-term quantum technologies.
Block-term Tensor Neural Networks
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e. g., image classification, natural language processing, etc.
Variational quantum Gibbs state preparation with a truncated Taylor series
By performing numerical experiments, we show that shallow parameterized circuits with only one additional qubit can be trained to prepare the Ising chain and spin chain Gibbs states with a fidelity higher than 95%.
Quantum Data Fitting Algorithm for Non-sparse Matrices
We propose a quantum data fitting algorithm for non-sparse matrices, which is based on the Quantum Singular Value Estimation (QSVE) subroutine and a novel efficient method for recovering the signs of eigenvalues.
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition
Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification.
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate.
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data.
