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Bharat Runwal

Alumni

Publications

APP: Anytime Progressive Pruning
Diganta Misra
Bharat Runwal
Tianlong Chen
Zhangyang Wang
Irina Rish
With the latest advances in deep learning, several methods have been investigated for optimal learning settings in scenarios where the data … (voir plus)stream is continuous over time. However, training sparse networks in such settings has often been overlooked. In this paper, we explore the problem of training a neural network with a target sparsity in a particular case of online learning: the anytime learning at macroscale paradigm (ALMA). We propose a novel way of progressive pruning, referred to as \textit{Anytime Progressive Pruning} (APP); the proposed approach significantly outperforms the baseline dense and Anytime OSP models across multiple architectures and datasets under short, moderate, and long-sequence training. Our method, for example, shows an improvement in accuracy of
2022-11-16
ACML.org/2022/Workshop/CLL (publié)
doi.org
openreview.net