15,722 research outputs found
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.Comment: Technical Repor
Characterizing the Shape of Activation Space in Deep Neural Networks
The representations learned by deep neural networks are difficult to
interpret in part due to their large parameter space and the complexities
introduced by their multi-layer structure. We introduce a method for computing
persistent homology over the graphical activation structure of neural networks,
which provides access to the task-relevant substructures activated throughout
the network for a given input. This topological perspective provides unique
insights into the distributed representations encoded by neural networks in
terms of the shape of their activation structures. We demonstrate the value of
this approach by showing an alternative explanation for the existence of
adversarial examples. By studying the topology of network activations across
multiple architectures and datasets, we find that adversarial perturbations do
not add activations that target the semantic structure of the adversarial class
as previously hypothesized. Rather, adversarial examples are explainable as
alterations to the dominant activation structures induced by the original
image, suggesting the class representations learned by deep networks are
problematically sparse on the input space
Input and Weight Space Smoothing for Semi-supervised Learning
We propose regularizing the empirical loss for semi-supervised learning by
acting on both the input (data) space, and the weight (parameter) space. We
show that the two are not equivalent, and in fact are complementary, one
affecting the minimality of the resulting representation, the other
insensitivity to nuisance variability. We propose a method to perform such
smoothing, which combines known input-space smoothing with a novel weight-space
smoothing, based on a min-max (adversarial) optimization. The resulting
Adversarial Block Coordinate Descent (ABCD) algorithm performs gradient ascent
with a small learning rate for a random subset of the weights, and standard
gradient descent on the remaining weights in the same mini-batch. It achieves
comparable performance to the state-of-the-art without resorting to heavy data
augmentation, using a relatively simple architecture
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