A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. (arXiv:2211.12386v1 [cs.LG])

Meta-learning of numerical algorithms for a given task consist of the
data-driven identification and adaptation of an algorithmic structure and the
associated hyperparameters. To limit the complexity of the meta-learning
problem, neural architectures with a certain inductive bias towards favorable
algorithmic structures can, and should, be used. We generalize our previously
introduced Runge-Kutta neural network to a recursively recurrent neural network
(R2N2) superstructure for the design of customized iterative algorithms. In
contrast to off-the-shelf deep learning approaches, it features a …

algorithms architecture arxiv iterative network neural network recurrent neural network