365 citations · 654 across the 11 of their papers we have counts for
4 papers · 1 filter
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon
Alexander Rakhlin, Xiyu Zhai
We show that minimum-norm interpolation in the Reproducing Kernel Hilbert Space corresponding to the Laplace kernel is not consistent if input dimension is constant. The lower boun…
Gradient Descent Finds Global Minima of Deep Neural Networks
Simon S. Du, Jason D. Lee, Haochuan Li +2
Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero tr…
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos +1
One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective fu…
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
Simon S. Du, Yining Wang, Xiyu Zhai +3
It is widely believed that the practical success of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) owes to the fact that CNNs and RNNs use a more compact…