[u' @inproceedings{almeida_embedding-level_2018, address = {Guanzhou, China}, title = {Embedding-level attention and multi-scale convolutional neural networks for behaviour modelling}, doi = {10.1109/SmartWorld.2018.00103}, abstract = {Understanding human behaviour is a central task in intelligent environments. Understanding what the user does and how she does it allows to build more reactive and smart environments. In this paper we present a new approach to interactivity behaviour modelling. This approach is based on the use of multi-scale convolutional neural networks to detect n-grams in action sequences and a novel method of applying soft attention mechanisms at embedding level. The proposed architecture improves our previous architecture based on recurrent networks, obtaining better result predicting the users\u2019 actions.}, author = {Almeida, Aitor and Azkune, Gorka and Bilbao Jayo, Aritz}, month = oct, year = {2018}, keywords = {AI for health, Artificial Intelligence, City4Age, Deep Learning, Intelligent Environments, Neural Networks, attention mechanism, behavior modelling, cnn, convolutional networks, core-b, embeddings, isi, machine learning}, } ']