Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cockpit: A Practical Debugging Tool for the Training of Deep Neural
  Networks   When engineers train deep learning models, they are very much 'flying blind'.
Commonly used methods for real-time training diagnostics, such as monitoring
the train/test loss, are limited. Assessing a network's training process solely
through these performance indicators is akin to debugging software without
access to internal states through a debugger. To address this, we present
Cockpit, a collection of instruments that enable a closer look into the inner
workings of a learning machine, and a more informative and meaningful status
report for practitioners. It facilitates the identification of learning phases
and failure modes, like ill-chosen hyperparameters. These instruments leverage
novel higher-order information about the gradient distribution and curvature,
which has only recently become efficiently accessible. We believe that such a
debugging tool, which we open-source for PyTorch, is a valuable help in
troubleshooting the training process. By revealing new insights, it also more
generally contributes to explainability and interpretability of deep nets.

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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an