PROPS: Probabilistic personalization of black-box sequence models

5 Mar 2019 Michael Thomas Wojnowicz Xuan Zhao

We present PROPS, a lightweight transfer learning mechanism for sequential data. PROPS learns probabilistic perturbations around the predictions of one or more arbitrarily complex, pre-trained black box models (such as recurrent neural networks)... (read more)

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METHOD TYPE
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
LSTM
Recurrent Neural Networks