Add to ORKGLeveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trained with stochastic gradient methods, ii) allows structured and multi-modal conditionals at each time step, iii) features a reliable estimator of the marginal likelihood and iv) is a generalisation of deter-ministic recurrent neural networks. We evaluate the method on four polyphonic musical data sets and motion capture data.
In the last few years, research highlighted the critical role of unsupervised pre-training strategie...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
Multilayer perceptrons (MLPs) or neural networks are popular models used for nonlinear regression an...
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with l...
Time series forecasting based on deep architectures has been gaining popularity in recent years due ...
Variational methods have been previously explored as a tractable approximation to Bayesian inference...
We present a model for capturing musical features and creating novel sequences of music, called the ...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
The ability to learn and perform statistical inference with biologically plausible recurrent network...
Multilayer perceptrons (MLPs) or artificial neural nets are popular models used for non-linear regre...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Deep probabilistic time series forecasting models have become an integral part of machine learning. ...
a b s t r a c t In the last few years, research highlighted the critical role of unsupervised pre-tr...
In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks ...
Abstract Recurrent neural networks have been success-fully used for analysis and prediction of tempo...
In the last few years, research highlighted the critical role of unsupervised pre-training strategie...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
Multilayer perceptrons (MLPs) or neural networks are popular models used for nonlinear regression an...
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with l...
Time series forecasting based on deep architectures has been gaining popularity in recent years due ...
Variational methods have been previously explored as a tractable approximation to Bayesian inference...
We present a model for capturing musical features and creating novel sequences of music, called the ...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
The ability to learn and perform statistical inference with biologically plausible recurrent network...
Multilayer perceptrons (MLPs) or artificial neural nets are popular models used for non-linear regre...
The paper studies a stochastic extension of continuous recurrent neural networks and analyzes gradie...
Deep probabilistic time series forecasting models have become an integral part of machine learning. ...
a b s t r a c t In the last few years, research highlighted the critical role of unsupervised pre-tr...
In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks ...
Abstract Recurrent neural networks have been success-fully used for analysis and prediction of tempo...
In the last few years, research highlighted the critical role of unsupervised pre-training strategie...
We investigate a novel neural network model which uses stochastic weights. It is shown that the func...
Multilayer perceptrons (MLPs) or neural networks are popular models used for nonlinear regression an...