4 research outputs found

    Data-Driven Representation Learning in Multimodal Feature Fusion

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    abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems. In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

    Revisi贸n sistem谩tica de sistemas inteligentes de transporte (ITS) a trav茅s de internet de las cosas (IOT) para problemas de transporte terrestre de pasajeros

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    Trabajo de Investigaci贸nEl desarrollo de este trabajo fue realizar una revisi贸n sistem谩tica de sistemas inteligentes de transporte (ITS) a trav茅s de internet de las cosas (IOT) para problemas de transporte terrestre de pasajeros, siguiendo la metodolog铆a de revisi贸n sistem谩tica de Barbara Kitchenham, definiendo palabras y frases para generar cadenas de busqueda e ir agregando criterios de inclusi贸n y exclusi贸n, en el proceso de b煤squeda en bases de datos cient铆ficas, con el fin de realizar un an谩lisis cuantitativo, mostrando una caracterizaci贸n de t茅rminos referentes a la investigaci贸n.INTRODUCCI脫N 1. GENERALIDADES 2. PLANIFICACION DE LA REVICION SISTEMATICA. 3. RESULTADOS CONCLUCIONES RECOMENDACIONES BIBLIOGRAF脥A ANEXOSPregradoIngeniero de Sistema
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