Today, the deep learning-based side-channel analysis represents a widely researched topic, with numerous results indicating the advantages of such an approach. Indeed, breaking protected ...

Hydro is a holistic system that automatically applies the hyperparameter transfer theory together with multiple system techniques to jointly improve the tuning efficiency. To learn more about how ...

Abstract: For distributed-drive electric vehicles, torque vectoring control based on model predictive control (MPC) has emerged as a preferred strategy to achieve superior performance across diverse ...

This toolbox enables hyperparameter optimization for autoencoders using a genetic algorithm. This framework extends the framework "Generic Deep Autoencoder for Time-Series" by providing an algorithm ...

In machine learning, algorithms harness the power to unearth hidden insights and predictions from within data. Central to the effectiveness of these algorithms are hyperparameters, which can be ...

Hyper-parameters are parameters used to regulate how the algorithm behaves while it creates the model. These factors cannot be discovered by routine training. Before the model is trained, it must be ...

A research team from Peking University, ETH Zürich and Kuaishou Technology proposes Hyper-Tune, an efficient and robust distributed hyperparameter-tuning framework that features system optimizations ...