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Found 4 papers, 0 papers with code
Joint Optimization of Energy Consumption and Completion Time in Federated Learning
Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics.
Deep self-consistent learning of local volatility
We present an algorithm for the calibration of local volatility from market option prices through deep self-consistent learning, by approximating both market option prices and local volatility using deep neural networks, respectively.
Reconstructing a dynamical system and forecasting time series by self-consistent deep learning
We introduce a self-consistent deep-learning framework which, for a noisy deterministic time series, provides unsupervised filtering, state-space reconstruction, identification of the underlying differential equations and forecasting.
Deep learning in physics: a study of dielectric quasi-cubic particles in a uniform electric field
The present work's objective is two-fold, first to show how an a priori knowledge can be incorporated into neural networks to achieve efficient learning and second to apply the method and study how the induced field and polarizability change when a dielectric particle progressively changes its shape from a sphere to a cube.
