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Machine Learning Foundation Papers2014

Neural Turing Machines

Bolts a differentiable, addressable memory bank onto a neural network and trains the whole thing with gradient descent, letting it learn algorithms like copying, sorting, and recall from examples — a learned computer rather than a fixed circuit.

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Introduction

Most neural networks of 2014 could pattern-match but couldn't really follow a procedure — they had no scratchpad to hold intermediate state across steps. This paper's quiet provocation was to take the textbook diagram of a computer — a controller plus addressable memory — and make every part of it differentiable, so the read/write logic could be learned by backprop instead of programmed. The result is less a model than a thought experiment that worked: a network that induces simple algorithms purely from input-output examples.

Key Findings
  • Memory as a separate, addressable resource. Decoupling computation (the RNN controller) from storage (an external memory matrix) means capacity scales without ballooning the parameter count — the same lesson that recurs in every later memory-augmented and retrieval system.
  • Two ways to point. Reads and writes blend content-based addressing (find rows that resemble a query) with location-based addressing (shift to an adjacent slot). That second mode is what makes iterating over a list or "moving to the next cell" learnable at all.
  • Everything is soft, so everything is trainable. Discrete operations — fetch, store, jump — are replaced by smooth, weighted attention over all locations, making the whole controller-memory loop end-to-end differentiable.
  • It generalizes by length. Trained on short sequences, NTMs extrapolate copy and sort to longer ones, evidence they learned an actual procedure rather than memorizing the training distribution.
How It Works

A recurrent controller emits read and write "heads" each step. Instead of indexing a single address, each head produces a soft distribution over all memory rows; writes erase and add along that distribution, reads return a weighted average. Addressing combines a content similarity score with learned gating and convolutional shifts, so the network can both look up by value and walk through memory positionally.

Why It Still Matters / When to Skip

Great fit if you want the conceptual root of memory-augmented networks, the Differentiable Neural Computer, and the broader "differentiable computer" lineage — and to see content-plus-location addressing before attention became ubiquitous. Look elsewhere if you need a practical recipe: NTMs are delicate to train and were superseded for most real tasks by Transformer attention, which kept the soft-addressing insight and dropped the explicit memory tape.

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Information

  • Websitear5iv.labs.arxiv.org
  • OrganizationsGoogle DeepMind
  • AuthorsAlex Graves, Greg Wayne, Ivo Danihelka
  • Published date2014/10/20

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