Title: From Nodes to Networks: Evolving Recurrent Neural Networks. Abstract: Gated recurrent networks such as those composed of Long Short-Term Memory
(LSTM) nodes have recently been used to improve state of the art in many sequential
processing tasks such as speech recognition and machine translation. However,
the basic structure of the LSTM node is essentially the same as when it was
first conceived 25 years ago. Recently, evolutionary and reinforcement learning
mechanisms have been employed to create new variations of this structure. This
paper proposes a new method, evolution of a tree-based encoding of the gated
memory nodes, and shows that it makes it possible to explore new variations more
effectively than other methods. The method discovers nodes with multiple recurrent
paths and multiple memory cells, which lead to significant improvement in the
standard language modeling benchmark task. Remarkably, this node did not perform
well in another task, music modeling, but it was possible to evolve a different
node that did, demonstrating that the approach discovers customized structure for
each task. The paper also shows how the search process can be speeded up by
training an LSTM network to estimate performance of candidate structures, and
by encouraging exploration of novel solutions. Thus, evolutionary design of complex
neural network structures promises to improve performance of deep learning
architectures beyond human ability to do so.