AdaNet: Adaptive structural learning of artificial neural networks
Abstract
We present new algorithms for adaptively learning
artificial neural networks. Our algorithms
(ADANET) adaptively learn both the structure
of the network and its weights. They are
based on a solid theoretical analysis, including
data-dependent generalization guarantees that we
prove and discuss in detail. We report the results
of large-scale experiments with one of our
algorithms on several binary classification tasks
extracted from the CIFAR-10 dataset and on the
Criteo dataset. The results demonstrate that our
algorithm can automatically learn network structures
with very competitive performance accuracies
when compared with those achieved by neural
networks found by standard approaches.
artificial neural networks. Our algorithms
(ADANET) adaptively learn both the structure
of the network and its weights. They are
based on a solid theoretical analysis, including
data-dependent generalization guarantees that we
prove and discuss in detail. We report the results
of large-scale experiments with one of our
algorithms on several binary classification tasks
extracted from the CIFAR-10 dataset and on the
Criteo dataset. The results demonstrate that our
algorithm can automatically learn network structures
with very competitive performance accuracies
when compared with those achieved by neural
networks found by standard approaches.
Research Areas
