  We study the problem of learning associative memory -- a system which is able
to retrieve a remembered pattern based on its distorted or incomplete version.
Attractor networks provide a sound model of associative memory: patterns are
stored as attractors of the network dynamics and associative retrieval is
performed by running the dynamics starting from a query pattern until it
converges to an attractor. In such models the dynamics are often implemented as
an optimization procedure that minimizes an energy function, such as in the
classical Hopfield network. In general it is difficult to derive a writing rule
for a given dynamics and energy that is both compressive and fast. Thus, most
research in energy-based memory has been limited either to tractable energy
models not expressive enough to handle complex high-dimensional objects such as
natural images, or to models that do not offer fast writing. We present a novel
meta-learning approach to energy-based memory models (EBMM) that allows one to
use an arbitrary neural architecture as an energy model and quickly store
patterns in its weights. We demonstrate experimentally that our EBMM approach
can build compressed memories for synthetic and natural data, and is capable of
associative retrieval that outperforms existing memory systems in terms of the
reconstruction error and compression rate.
