research
∙
05/03/2023
Dynamic Sparse Training with Structured Sparsity
DST methods achieve state-of-the-art results in sparse neural network tr...
share
research
∙
07/19/2022
Bounding generalization error with input compression: An empirical study with infinite-width networks
Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) ...
research
∙
06/27/2022
Monitoring Shortcut Learning using Mutual Information
The failure of deep neural networks to generalize to out-of-distribution...
research
∙
05/24/2016
Measuring Neural Net Robustness with Constraints
Despite having high accuracy, neural nets have been shown to be suscepti...
research
∙
05/20/2016
Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
We propose a new method for creating computationally efficient and compa...
research
∙
04/22/2016
Refining Architectures of Deep Convolutional Neural Networks
Deep Convolutional Neural Networks (CNNs) have recently evinced immense ...
research
∙
03/03/2016
Decision Forests, Convolutional Networks and the Models in-Between
This paper investigates the connections between two state of the art cla...
research
∙
11/20/2015
Training CNNs with Low-Rank Filters for Efficient Image Classification
We propose a new method for creating computationally efficient convoluti...
research
∙
09/08/2012