1,754 research outputs found
Adversarial Dropout for Supervised and Semi-supervised Learning
Recently, the training with adversarial examples, which are generated by
adding a small but worst-case perturbation on input examples, has been proved
to improve generalization performance of neural networks. In contrast to the
individually biased inputs to enhance the generality, this paper introduces
adversarial dropout, which is a minimal set of dropouts that maximize the
divergence between the outputs from the network with the dropouts and the
training supervisions. The identified adversarial dropout are used to
reconfigure the neural network to train, and we demonstrated that training on
the reconfigured sub-network improves the generalization performance of
supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We
analyzed the trained model to reason the performance improvement, and we found
that adversarial dropout increases the sparsity of neural networks more than
the standard dropout does.Comment: submitted to AAAI-1
Metrics for Graph Comparison: A Practitioner's Guide
Comparison of graph structure is a ubiquitous task in data analysis and
machine learning, with diverse applications in fields such as neuroscience,
cyber security, social network analysis, and bioinformatics, among others.
Discovery and comparison of structures such as modular communities, rich clubs,
hubs, and trees in data in these fields yields insight into the generative
mechanisms and functional properties of the graph.
Often, two graphs are compared via a pairwise distance measure, with a small
distance indicating structural similarity and vice versa. Common choices
include spectral distances (also known as distances) and distances
based on node affinities. However, there has of yet been no comparative study
of the efficacy of these distance measures in discerning between common graph
topologies and different structural scales.
In this work, we compare commonly used graph metrics and distance measures,
and demonstrate their ability to discern between common topological features
found in both random graph models and empirical datasets. We put forward a
multi-scale picture of graph structure, in which the effect of global and local
structure upon the distance measures is considered. We make recommendations on
the applicability of different distance measures to empirical graph data
problem based on this multi-scale view. Finally, we introduce the Python
library NetComp which implements the graph distances used in this work
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