Go Beyond Multiple Instance Neural Networks: Deep-learning Models based on Local Pattern Aggregation. (arXiv:2205.14428v2 [cs.LG] UPDATED)

Deep convolutional neural networks (CNNs) have brought breakthroughs in
processing clinical electrocardiograms (ECGs), speaker-independent speech and
complex images. However, typical CNNs require a fixed input size while it is
common to process variable-size data in practical use. Recurrent networks such
as long short-term memory (LSTM) are capable of eliminating the restriction,
but suffer from high computational complexity. In this paper, we propose local
pattern aggregation-based deep-learning models to effectively deal with both
problems. The novel network structure, called LPANet, has …

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