AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Corinna Cortes ⋅ Xavi Gonzalvo ⋅ Vitaly Kuznetsov ⋅ Mehryar Mohri ⋅ Scott Yang
2017 Poster
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Abstract
We present a new framework for analyzing and learning artificial neural networks. Our approach simultaneously and adaptively learns both the structure of the network as well as its weights. The methodology is based upon and accompanied by strong data-dependent theoretical learning guarantees, so that the final network architecture provably adapts to the complexity of any given problem.
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