Proposes GP-RNN model using RNNs for nonlinear non-Markovian dynamics and GPs for embedding, with bi-LSTM inference, that outperforms prior methods on neural data especially with limited samples.
Pattern recognition and machine learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Proposes GP-RNN model using RNNs for nonlinear non-Markovian dynamics and GPs for embedding, with bi-LSTM inference, that outperforms prior methods on neural data especially with limited samples.