288 pp. per issue
6 x 9, illustrated
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Neural Computation

April 2018, Vol. 30, No. 4, Pages 857-884
(doi: 10.1162/neco_a_01060)
© 2018 Massachusetts Institute of Technology
Dynamic Neural Turing Machine with Continuous and Discrete Addressing Schemes
Article PDF (395.43 KB)
We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies, including both linear and nonlinear ones. We implement the D-NTM with both continuous and discrete read and write mechanisms. We investigate the mechanisms and effects of learning to read and write into a memory through experiments on Facebook bAbI tasks using both a feedforward and GRU controller. We provide extensive analysis of our model and compare different variations of neural Turing machines on this task. We show that our model outperforms long short-term memory and NTM variants. We provide further experimental results on the sequential MNIST, Stanford Natural Language Inference, associative recall, and copy tasks.