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Found 3 papers, 1 papers with code
A Bayesian Variational principle for dynamic Self Organizing Maps
We propose organisation conditions that yield a method for training SOM with adaptative neighborhood radius in a variational Bayesian framework.
A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)
We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE).
Representation Learning in Partially Observable Environments using Sensorimotor Prediction
We propose a model which integrates sensorimotor information over time, and project it in a sensory representation which is useful for prediction.
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