Compressing the Gram Matrix for Learning Neural Networks in Polynomial Time
neural information processing systems, pp. 2189-2199, 2017.
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 intractable in the worst-case. A major open problem is to understand the minimal assumptions under which these classes admit efficient algorithms. In this work we show that a natural distr...More
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