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Found 4 papers, 2 papers with code
Preconditioned Federated Learning
Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner.
Scalable Quantum Neural Networks for Classification
The quantum feature extractors in the SQNN system are independent of each other, so one can flexibly use quantum devices of varying sizes, with larger quantum devices extracting more local features.
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG.
A new perspective in understanding of Adam-Type algorithms and beyond
Moreover, we provide new variant of Adam-Type algorithm, namely AdamAL which can naturally mitigate the non-convergence issue of Adam and improve its performance.
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