  Much of the recent research on solving iterative inference problems focuses
on moving away from hand-chosen inference algorithms and towards learned
inference. In the latter, the inference process is unrolled in time and
interpreted as a recurrent neural network (RNN) which allows for joint learning
of model and inference parameters with back-propagation through time. In this
framework, the RNN architecture is directly derived from a hand-chosen
inference algorithm, effectively limiting its capabilities. We propose a
learning framework, called Recurrent Inference Machines (RIM), in which we turn
algorithm construction the other way round: Given data and a task, train an RNN
to learn an inference algorithm. Because RNNs are Turing complete [1, 2] they
are capable to implement any inference algorithm. The framework allows for an
abstraction which removes the need for domain knowledge. We demonstrate in
several image restoration experiments that this abstraction is effective,
allowing us to achieve state-of-the-art performance on image denoising and
super-resolution tasks and superior across-task generalization.
