  There has been a recent trend in training neural networks to replace data
structures that have been crafted by hand, with an aim for faster execution,
better accuracy, or greater compression. In this setting, a neural data
structure is instantiated by training a network over many epochs of its inputs
until convergence. In applications where inputs arrive at high throughput, or
are ephemeral, training a network from scratch is not practical. This motivates
the need for few-shot neural data structures. In this paper we explore the
learning of approximate set membership over a set of data in one-shot via
meta-learning. We propose a novel memory architecture, the Neural Bloom Filter,
which is able to achieve significant compression gains over classical Bloom
Filters and existing memory-augmented neural networks.
