Lie-Access Neural Turing Machines

Greg Yang, Alexander Rush

Abstract:

External neural memory structures have recently become a popular tool for
algorithmic deep learning
(Graves et al. 2014; Weston et al. 2014). These models
generally utilize differentiable versions of traditional discrete
memory-access structures (random access, stacks, tapes) to provide
the storage necessary for computational tasks. In
this work, we argue that these neural memory systems lack specific
structure important for relative indexing, and propose an
alternative model, Lie-access memory, that is explicitly designed
for the neural setting. In this paradigm, memory is accessed using
a continuous head in a key-space manifold. The head is moved via Lie
group actions, such as shifts or rotations, generated by a
controller, and memory access is performed by linear smoothing in
key space. We argue that Lie groups provide a natural generalization
of discrete memory structures, such as Turing machines, as they
provide inverse and identity operators while maintaining
differentiability. To experiment with this approach, we implement
a simplified Lie-access neural Turing machine (LANTM) with
different Lie groups. We find that this approach is able to perform
well on a range of algorithmic tasks.

TL;DR: We generalize Turing machines to the continuous setting using Lie group actions on manifolds.

Conflicts: harvard.edu

Keywords: Natural language processing, Deep learning, Supervised Learning

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1611.02854/code)