Sequential Monte Carlo Methods to Train Neural Network Models
We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent / sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequenti...
Main Authors: | Freitas, D, Nando, Niranjan, M, Gee, A, Doucet, A |
---|---|
Formato: | Journal article |
Publicado em: |
2000
|
Registos relacionados
-
Sequential monte carlo methods To train neural network models
Por: , d, et al.
Publicado em: (2000) -
Sequential Monte Carlo methods for diffusion processes
Por: Jasra, A, et al.
Publicado em: (2009) -
Sequential Monte Carlo samplers
Por: Del Moral, P, et al.
Publicado em: (2006) -
Sequential Monte Carlo for model selection and estimation of neural networks
Por: Andrieu, C, et al.
Publicado em: (2000) -
Controlled sequential Monte Carlo
Por: Heng, J, et al.
Publicado em: (2020)