A Spatiotemporal Context-free Approach to Large-scale Multiview Human Trafficking Cases and Detections


A Spatiotemporal Context-free Approach to Large-scale Multiview Human Trafficking Cases and Detections – We investigate the problem of training multichannel data-generating neural networks for joint decision making, and compare it to other machine learning approaches in a variety of real world domains. The model trained to learn a mapping from its input data to a global representation is trained by either single frame training or multichannel training with a few frames per label. Our learning procedure, learned from multiple frames and multiple labels, takes into account how the different models are learned so that the output from one or multiple models is best suited for each scenario. The learning procedure is a step closer to formalizing the multichannel approach we propose in the paper. We propose two novel algorithms to train and evaluate the multichannel approach, which are not only effective for joint decision making but also can be used to improve end-to-end learning.

It is common that solving problems with nonnegative matrix factors with finite sample size are extremely difficult and time-consuming for non-convex systems. However, as matrix factorization is an unsupervised learning algorithm, it is far from being standard for the supervised problem. In this paper, we propose a novel approach for solving nonnegative matrix factorization problems using unsupervised learning where the nonnegative matrix factors are generated by solving an unsupervised optimization problem. We demonstrate that this approach is very suitable for the problem of learning nonnegative matrix factorization under a nonparametric framework. We also demonstrate its effectiveness on the data acquisition problem, showing that the approach can be used to obtain very close to state-of-the-art results in the literature.

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A Spatiotemporal Context-free Approach to Large-scale Multiview Human Trafficking Cases and Detections

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  • Learning to See, Hear and Read Human-Object Interactions

    Robust Nonnegative Matrix Factorization via Non-convex Matrix RegularizationIt is common that solving problems with nonnegative matrix factors with finite sample size are extremely difficult and time-consuming for non-convex systems. However, as matrix factorization is an unsupervised learning algorithm, it is far from being standard for the supervised problem. In this paper, we propose a novel approach for solving nonnegative matrix factorization problems using unsupervised learning where the nonnegative matrix factors are generated by solving an unsupervised optimization problem. We demonstrate that this approach is very suitable for the problem of learning nonnegative matrix factorization under a nonparametric framework. We also demonstrate its effectiveness on the data acquisition problem, showing that the approach can be used to obtain very close to state-of-the-art results in the literature.


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