Jul 12, 2020
We introduce "instability analysis," which assesses whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise. We find that standard vision models become stable in this way early in training. From then on, the outcome of optimization is determined to within a linearly connected region. We use instability to study iterative magnitude pruning (IMP), the procedure used by work on the lottery ticket hypothesis to identify subnetworks that could have trained to full accuracy from initialization. We find that these subnetworks only reach full accuracy when they are stable, which either occurs at initialization for small-scale settings (MNIST) or early in training for large-scale settings (Resnet-50 and Inception-v3 on ImageNet).
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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