Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks

neural information processing systems, pp. 2189-2199, 2017.

Cited by: 20|Views18


We consider the problem of learning function classes computed by neural networks with various activations (e.g. ReLU or Sigmoid), a task believed to be computationally intractable in the worst-case. A major open problem is to understand the minimal assumptions under which these classes admit provably efficient algorithms. In this work we ...More



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