Abstract: Machine learning is increasingly using multiview learning. Most multiview learning methods cannot directly handle multiview sequential data, which often ignores dynamical structure. Most traditional multiview machine learning methods assume that sequence items at different…
Abstract: Pattern recognition, data mining, and machine learning all face clustering issues. Traditional shallow-structured clustering algorithms cannot uncover the interdependence of complex data features in latent space. Since they can learn promising latent representations from…
Abstract: Most scientific fields estimate mathematical model parameters. When processes and model descriptions become complex and there is no explicit likelihood function, this problem can be difficult. We propose BayesFlow, an invertible neural network-based globally…
Abstract: Many mobile social network users share messages. Spammers post links to viruses and ads or follow many users, resulting in many misleading messages in mobile social networks. This paper proposes an adaptive social spammer…
Abstract: Data matter. How can we quantitatively and objectively evaluate data? Pricing data, or information goods, has been studied and practiced in many disciplines, including economics, marketing, electronic commerce, data management, data mining, and machine…
Abstract: Pediatric neuroblastoma is deadly. Treatment plans depend on neuroblastoma survival prediction. We proposed a heterogeneous ensemble learning method to predict neuroblastoma patient survival and extracted decision rules to help doctors make decisions. Five heterogeneous…
Abstract: Active learning (AL) queries labels from unlabeled data to improve classification hypothesis generalization. Informative, representative, or diverse evaluation policies evaluate sampling. In a cold-start hypothesis, the policy, which requires an initial labeled set, may…
Abstract: Machine learning and data processing use nonconvex regularized optimization problems. The consensus optimization used the iteratively reweighted algorithm due to problem structure. This paper proposes adding an inertial term to each iteration to accelerate…
Abstract: Scene parsing, robot motion planning, and autonomous driving require accurate scene classification. Deep recognition models have performed well for a decade. However, these deep architectures cannot encode human visual perception, including gaze movements and…
Abstract: Deep neural networks (DNNs) solve complex machine learning issues. Function representation’s expressiveness helped them succeed. Since piecewise linear neural networks (PLNNs) use linear pieces to model complex patterns, the number of linear regions is…
Abstract: We design a platform for prototyping low-cost analog neural networks for on-chip integration with analog/RF circuits. Integration supports self-test, self-tuning, and trust/aging monitoring by classifying analog measurements from on-chip sensors. 1) Low energy and…
Abstract: Semi-supervised learning is one of the most appealing machine learning problems. This paper introduces Semi-supervised Adaptive Local Embedding learning (SALE), a locality-preserved dimensionality reduction framework that learns a local discriminative embedding by constructing a…
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