5.5k citations · 6.6k across the 5 of their papers we have counts for
5 papers
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Hongyang Li, David Eigen, Samuel Dodge +2
Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limit…
Visualizing and Understanding Convolutional Networks
Matthew D Zeiler, Rob Fergus
Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they p…
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…
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…
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…