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Dropout as Bayesian Approximation in RNN

Master thesis - Aalto University - Machine Learning and Data Science

Dataset

TODOs

  • Add biases
  • Make loss function
  • Stack multiple LSTM layers
  • Handle single dimension input
  • Iterate Stochastic pass once during training
  • Use parameter optimizer instead of writing custom loss functions
  • Make stochastic modules for regression
  • Make stochastic modules for classification
  • Compute predictive variance for classification
  • Compute predictive variance for regression
  • Optimize dropout rate (using Concrete Dropout)
  • Download clinical data
  • Early stop in training (use validation error)
  • Make equivalent in Tensorflow