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Understanding intermediate layers using linear classifier probes
Guillaume Alain, Yoshua Bengio
Feb 17, 2017 (modified: Feb 17, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:Neural network models have a reputation for being black boxes.
We propose a new method to better understand the roles and dynamics
of the intermediate layers.
Our method uses linear classifiers, referred to as "probes",
where a probe can only use the hidden units of a given intermediate layer
as discriminating features.
Moreover, these probes cannot affect the training phase of a model,
and they are generally added after training.
We demonstrate how this can be used to develop a better intuition
about models and to diagnose potential problems.
TL;DR:Investigating deep learning models by proposing a different concept of information
Keywords:Deep learning, Supervised Learning, Theory
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