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Found 3 papers, 2 papers with code
A Structure-Preserving Kernel Method for Learning Hamiltonian Systems
A structure-preserving kernel ridge regression method is presented that allows the recovery of potentially high-dimensional and nonlinear Hamiltonian functions out of datasets made of noisy observations of Hamiltonian vector fields.
Detecting data-driven robust statistical arbitrage strategies with deep neural networks
We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets.
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
Then we propose two optimization methods: (1) To address the unbalanced weight problem, we propose a new method to dynamically change the weights according to the scale of model changes in clients in each training round; (2) To facilitate convergence, we adopt knowledge distillation to refine the server model with knowledge generated from client models on a public dataset.
