365 citations · 452 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
Yifan Wu, Barnabas Poczos, Aarti Singh
A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that h…
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Simon S. Du, Jason D. Lee, Yuandong Tian +2
We consider the problem of learning a one-hidden-layer neural network with non-overlapping convolutional layer and ReLU activation, i.e., $f(\mathbf{Z}, \mathbf{w}, \mathbf{a}) = \…