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Face detection in unconstrained environments is a challenging problem due to partial occlusions with pose variations. Existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. In this paper, our contributions are two-fold. First, we propose our Large-Scale Deep Learning (LSDL), a method that requires a single Convolutional Neural Network...
Different modalities have been proved to carry various information. This paper aims to study how the multiple face regions/channels and multiple models (e.g., hand-crafted and unsupervised learning methods) answer to the face recognition problem. Hand crafted and deep feature learning techniques have been proposed and applied to estimate discriminative features in object recognition problems. In our...
High-throughput technologies used to interrogate transcriptomes have been generating a great amount of publicly available gene expression data. For rare diseases that lack of clinical samples and research funding, there is a practical benefit to jointly analyze existing data sets commonly related to a specific rare disease. In this study, we collected a number of independently generated transcriptome...
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