Abstract: A computer-implemented method includes receiving data generated using at least one sensor of a vehicle; and simultaneously performing multiple different prediction tasks on the data using a multi-task neural network, wherein the multi-task neural network comprises at least one shared parameter inference matrix comprising parameters shared between the multiple different prediction tasks, and the at least one shared parameter inference matrix was over-parameterized during training into at least one shared parameter matrix and multiple task-specific parameter matrices, each of the multiple task-specific parameter matrices being associated with a different one of the multiple different tasks.

Abstract: Described herein is a framework for outdoor localization. In accordance with one aspect of the framework, a set of hotspot labels are received from one or more user devices connected to an outdoor wireless local area network. Manifold learning may be performed based on the set of hotspot labels to construct one or more manifolds. Using the one or more constructed manifolds, the framework may then estimate a location of a particular user device associated with a query record received from during an online location query.

Abstract: Methods, systems, and computer-readable storage media for receiving input data including a set of sentences, each sentence including computer-readable text as a sequence of tokens, providing a memory network with coupled attentions (MNCA), the coupled attentions including an aspect attention and an opinion attention that are coupled by tensor operators for each sentence in the set of sentences, processing the input data through the MNCA to identify a set of aspect terms, and a set of opinion terms, and simultaneously assign a category to each aspect term and each opinion term from a set of categories, and outputting the set of aspect terms with respective categories, and the set of opinion terms with respective categories.

Abstract: Described herein is a framework for outdoor localization. In accordance with one aspect of the framework, a set of hotspot labels are received from one or more user devices connected to an outdoor wireless local area network. Manifold learning may be performed based on the set of hotspot labels to construct one or more manifolds. Using the one or more constructed manifolds, the framework may then estimate a location of a particular user device associated with a query record received from during an online location query.

Abstract: Described herein is a framework to perform aspect-based sentiment analysis. In accordance with one aspect of the framework, initial word embeddings are generated from a training dataset. A predictive model is trained using the initial word embeddings. The trained predictive model may then be used to recognize one or more sequences of tokens in a current dataset.