Search Results for author:
Found 5 papers, 1 papers with code
Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition
We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule.
Semi-supervised learning with Bayesian Confidence Propagation Neural Network
Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data.
StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas.
Brain-like approaches to unsupervised learning of hidden representations -- a comparative study
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years.
Learning representations in Bayesian Confidence Propagation neural networks
Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years.
