93 citations · 96 across the 4 of their papers we have counts for
4 papers
Learnability of Linear Port-Hamiltonian Systems
Juan-Pablo Ortega, Daiying Yin
A complete structure-preserving learning scheme for single-input/single-output (SISO) linear port-Hamiltonian systems is proposed. The construction is based on the solution, when p…
Expressiveness and Structure Preservation in Learning Port-Hamiltonian Systems
Juan-Pablo Ortega, Daiying Yin
A well-specified parametrization for single-input/single-output (SISO) linear port-Hamiltonian systems amenable to structure-preserving supervised learning is provided. The constru…
Detecting data-driven robust statistical arbitrage strategies with deep neural networks
Ariel Neufeld, Julian Sester, Daiying Yin
We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets. Robust statistical arbitrage strategies…
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
Weiming Zhuang, Yonggang Wen, Xuesen Zhang +5
Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. It can alleviate privacy concerns of personal re-iden…