A masking regularizer (coordinated dropout) and a sample-based validation metric make sequential autoencoders for spiking data tunable with small datasets.
Bayesian learning and inference in recurrent switching linear dynamical systems
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Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
A masking regularizer (coordinated dropout) and a sample-based validation metric make sequential autoencoders for spiking data tunable with small datasets.