M
Matthew D. Zeiler
Researcher at New York University
Publications - 23
Citations - 29431
Matthew D. Zeiler is an academic researcher from New York University. The author has contributed to research in topics: Convolutional neural network & Softmax function. The author has an hindex of 16, co-authored 23 publications receiving 24598 citations.
Papers
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Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Posted Content
ADADELTA: An Adaptive Learning Rate Method
TL;DR: A novel per-dimension learning rate method for gradient descent called ADADELTA that dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent is presented.
Posted Content
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: In this article, the authors introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier, and perform an ablation study to discover the performance contribution from different model layers.
Proceedings Article
Regularization of Neural Networks using DropConnect
TL;DR: This work introduces DropConnect, a generalization of Dropout, for regularizing large fully-connected layers within neural networks, and derives a bound on the generalization performance of both Dropout and DropConnect.
Proceedings Article
Deconvolutional networks
TL;DR: This work presents a learning framework where features that capture these mid-level cues spontaneously emerge from image data, based on the convolutional decomposition of images under a spar-sity constraint and is totally unsupervised.