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Found 4 papers, 2 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).
Latent Space Data Assimilation by using Deep Learning
Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling, particularly at the time of big data where huge quantities of observations are available.
Data Assimilation Networks
Data assimilation (DA) aims at forecasting the state of a dynamical system by combining a mathematical representation of the system with noisy observations taking into account their uncertainties.
