Web: http://arxiv.org/abs/2201.09953

Jan. 26, 2022, 2:10 a.m. | Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, Anita Raja

cs.LG updates on arXiv.org arxiv.org

Efficiency is essential to support responsiveness w.r.t. ever-growing
datasets, especially for Deep Learning (DL) systems. DL frameworks have
traditionally embraced deferred execution-style DL code that supports symbolic,
graph-based Deep Neural Network (DNN) computation. While scalable, such
development tends to produce DL code that is error-prone, non-intuitive, and
difficult to debug. Consequently, more natural, less error-prone imperative DL
frameworks encouraging eager execution have emerged but at the expense of
run-time performance. While hybrid approaches aim for the "best of both
worlds," …

arxiv deep deep learning graph learning study

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