Towards Adaptive Residual Network Training: A Neural-ODE Perspective

Jul 12, 2020



Serving as a crucial factor, the depth of residual networks balances model capacity, performance, and training efficiency. However, depth has been long fixed as a hyper-parameter and needs laborious tuning, due to the lack of theories describing its dynamics. Here, we conduct theoretical analysis on network depth and introduce adaptive residual network training, which gradually increases model depth during training. Specifically, from an ordinary differential equation perspective, we describe the effect of depth growth with embedded errors, characterize the impact of model depth with truncation errors, and derive bounds for them. Illuminated by these derivations, we propose an adaptive training algorithm for residual networks, LipGrow, which automatically increases network depth and accelerates model training. In our experiments, it achieves better or comparable performance while reducing  50



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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