563 citations · 646 across the 8 of their papers we have counts for
5 papers · 1 filter
Importance Estimation for Neural Network Pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree +2
Structural pruning of neural network parameters reduces computation, energy, and memory transfer costs during inference. We propose a novel method that estimates the contribution o…
Exact Gaussian Processes on a Million Data Points
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner +3
Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data. However, computational constraints with standard inference procedur…
Compressing Convolutional Neural Networks
Wenlin Chen, James T. Wilson, Stephen Tyree +2
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities o…
Compressing Neural Networks with the Hashing Trick
Wenlin Chen, James T. Wilson, Stephen Tyree +2
As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-in…
Compressed Support Vector Machines
Zhixiang Xu, Jacob R. Gardner, Stephen Tyree +1
Support vector machines (SVM) can classify data sets along highly non-linear decision boundaries because of the kernel-trick. This expressiveness comes at a price: During test-time…