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Neural networks (NNs) struggle to efficiently solve certain problems, such aslearning parities, even when there are simple learning algorithms for thoseproblems. Can NNs discover learning algorithms on their own? We exhibit a NNarchitecture that, in polynomial time, learns as well as any efficient learningalgorithm describable by a constant-sized program. For example, on parityproblems, the NN learns as well as Gaussian elimination, an efficient algorithmthat can be succinctly described. Our architecture combines both recurrentweight sharing between layers and convolutional weight sharing to reduce thenumber of parameters down to a constant, even though the network itself mayhave trillions of nodes. While in practice the constants in our analysis aretoo large to be directly meaningful, our work suggests that the synergy ofRecurrent and Convolutional NNs (RCNNs) may be more natural and powerful thaneither alone, particularly for concisely parameterizing discrete algorithms.

Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms