However, Digital Twins are data intensive and need highly localized data, making them difficult to scale, particularly to small cities, and with the high cost associated to data collection.
This new trainable layer is capable of coping with image classification even with large contrast variations.
Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development.
WordNet, which includes a wide variety of concepts associated with words (i. e., synsets), is often used as a source for computing those distances.
Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes.
In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme.
Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.
We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction.
Data Structures and Algorithms Social and Information Networks Physics and Society
We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning.
Link prediction, the problem of identifying missing links among a set of inter-related data entities, is a popular field of research due to its application to graph-like domains.