We propose two solutions to outlier detection in time series based on recurrent autoencoder
ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent …

We study the problem of knowledge graph (KG) embedding. A widely-established
assumption to this problem is that similar entities are likely to have similar relational roles …

Teaching plays a very important role in our society, by spreading human knowledge and
educating our next generations. A good teacher will select appropriate teaching materials …

The classification of natural language texts has gained a growing importance in many real
world applications due to its significant implications in relation to crucial tasks, such as …

In this paper, we are interested in exploiting textual and acoustic data of an utterance for the
speech emotion classification task. The baseline approach models the information from …

We propose a general strategy named 'divide, conquer and combine'for multimodal fusion.
Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that …

The use of containers in cloud computing has been steadily increasing. With the emergence
of Kubernetes, the management of applications inside containers (or pods) is simplified …

Recurrent neural network (RNN) has achieved remarkable performance in text
categorization. RNN can model the entire sequence and capture long-term dependencies …

Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as
basic recurrent unit. However, conventional LSTM assumes that the state at current time step …

Large-batch training approaches have enabled researchers to utilize distributed processing
and greatly accelerate deep neural networks training. However, there are three problems in …