Deepest Neural Networks
arXiv:1707.02617v14 citations
Originality Synthesis-oriented
AI Analysis
This provides theoretical insight into the capabilities of deep, narrow neural networks for classification tasks.
The paper demonstrates that a multilayer perceptron with many narrow hidden layers can serve as a universal classifier, though it may not be computationally efficient.
This paper shows that a long chain of perceptrons (that is, a multilayer perceptron, or MLP, with many hidden layers of width one) can be a universal classifier. The classification procedure is not necessarily computationally efficient, but the technique throws some light on the kind of computations possible with narrow and deep MLPs.