minimising contrastive divergence
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons
This paper presents VLSI circuits with continuous-valued proba- bilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons forms a Contin- uous Restricted Boltzmann Machine (CRBM), which has shown promising performance in modelling and classifying noisy biomed- ical data.
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons
Chen, Hsin, Fleury, Patrice, Murray, Alan F.
This paper presents VLSI circuits with continuous-valued probabilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons forms a Continuous Restricted Boltzmann Machine (CRBM), which has shown promising performance in modelling and classifying noisy biomedical data.
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons
Chen, Hsin, Fleury, Patrice, Murray, Alan F.
This paper presents VLSI circuits with continuous-valued probabilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons forms a Continuous Restricted Boltzmann Machine (CRBM), which has shown promising performance in modelling and classifying noisy biomedical data.
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons
Chen, Hsin, Fleury, Patrice, Murray, Alan F.
VLSI implementation of the noisy neuron and the MOD learning rule are presented.