5.5k citations · 6.1k across the 3 of their papers we have counts for
3 papers
cs.LG2013★ 575 cited
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler, Rob Fergus
We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic proc…
cs.LG2012★ 5.5k cited
ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minim…
cs.CV2012★ 9 cited
Differentiable Pooling for Hierarchical Feature Learning
Matthew D. Zeiler, Rob Fergus
We introduce a parametric form of pooling, based on a Gaussian, which can be optimized alongside the features in a single global objective function. By contrast, existing pooling s…