Using Fast Weights to Attend to the Recent Past

Reproducing the associative model experiment on the paper

Using Fast Weights to Attend to the Recent Past by Jimmy Ba et al. (Incomplete)

Prerequisites

Tensorflow (version >= 0.8)

How to Run the Experiments

Generate a dataset

$ python generator.py

This script generates a file called associative-retrieval.pkl, which can be used for training.

Run the model

$ python fw.py

Findings

Currently, we are able to see that the accuracy easily exceeds 0.9 for R=20, and 0.97 for R=50, which can justify for the effectiveness of the model. The experiments are barely tuned.

Layer Normalization is extremely crucial for the success of training.

• Otherwise, training will not converge when the inner step is larger than 1.
• Even when inner step of 1, the performance without layer normalization is much worse. For R=20, only 0.4 accuracy can be achieved (which is same as the level of other models.)
• Even with Layer Normalization, using slow weights (ie. vanilla RNN) is much worse than using fast weights.

Further improvements:

• Complete fine-tuning
• Use accelerated version of A
• Add visualization

References

Using Fast Weights to Attend to the Recent Past. Jimmy Ba,  Geoffrey Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu.

Layer Normalization. Jimmy Ba, Ryan Kiros, Geoffery Hinton.