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Found 4 papers, 2 papers with code
A Fortran-Keras Deep Learning Bridge for Scientific Computing
Implementing artificial neural networks is commonly achieved via high-level programming languages like Python and easy-to-use deep learning libraries like Keras.
Exploring the Efficacy of Transfer Learning in Mining Image-Based Software Artifacts
Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task.
Learning in the Machine: To Share or Not to Share?
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes.
Mining Internet-Scale Software Repositories
Large repositories of source code create new challenges and opportunities for statistical machine learning.
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