On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.
We conducted extensive simulation studies, showing that our model is robust to the degree of sparsity and improves classification accuracy when the data have multiway structure.
To address these issues, we propose DeepExpress - a deep-learning based express delivery sequence prediction model, which extends the classic seq2seq framework to learning complex coupling between sequence and features.
To solve the imbalanced distribution problem, in this paper we propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection.
There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.
Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life.
In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection.
In recent years, with the development of deep learning, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication.
A generic model for CrowdMining is further proposed based on a set of existing studies.
In fact, user's behaviors from different domains regarding the same items are usually relevant.
And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.
In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning.
To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm.