Global Optimisation of Neural Network Models via Sequential Sampling

We propose a novel strategy for training neural networks using sequential Monte Carlo algorithms. This global optimisation strategy allows us to learn the probability distribution of the network weights in a sequential framework. It is well suited to applications involving on-line, nonlinear or non-stationary signal processing. We show how the new algorithms can outperform extended Kalman filter (EKF) training.

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee
• Keywords: Global Optimisation Strategy, NIPS 1998, NIPS 2007, Sequential Monte Carlo, Training Neural Networks