Hidden Memories of RNNs

Understanding RNNs: lack of interpretable models, hidden states, and the role of memory.

• Performance based: alter the components and see how it affects the accuracy.
• Interpretability extension: visualization (and comparative clustering), use adjacency metrics.
  • Justaposition: detail / sentence / overview level
  • Superposition:
  • Explicit encoding

Requirements

• Interpret information captured by hidden states / layers

• Provide information distribution

• Explore hidden states at sentence level

• Examine stats for hidden states

• See learning outcomes

• See distribution of hidden states

• See model expected response based on update of cell

• C = maintains long term memory

• h = directly computed from cell state and used for output

• Dc = distribution of cell states change

Co cluster hidden states and words to see how they are related. Also sequence analysis to see how hidden states change over time.