Winner does not take all: Selective attention and local bias in platform-based markets
Sign Up to like & getrecommendations! Published in 2017 at "Technological Forecasting and Social Change"
DOI: 10.1016/j.techfore.2016.08.028
Abstract: We model how macro-level dynamics of platform competition emerge from micro-level interactions among consumers. We problematize the prevailing winner-take-all hypothesis and argue that instead of assuming that consumers value the general connectivity of an entire… read more here.
Keywords: take selective; selective attention; local bias; attention local ... See more keywords
Local Augment: Utilizing Local Bias Property of Convolutional Neural Networks for Data Augmentation
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3050758
Abstract: Data augmentation is an effective way to increase the diversity of existing training datasets that result in improved generalization ability of convolutional neural networks (CNNs). The augmentation effect is usually global for the existing methods… read more here.
Keywords: augmentation; local bias; bias property; data augmentation ... See more keywords