Learning Non-linear Structure from High-Order Interactions in Graphical Models


Learning Non-linear Structure from High-Order Interactions in Graphical Models – A very popular approach to modeling problems involving non-linear interactions involves the use of multiple variables of the same type, which are usually independent. Motivated by this model, we study the problem of univariate non-linear interaction, where interacting variables have to be mutually related with each other. The objective is to estimate the interactions of the two variables. We demonstrate that this problem can be successfully solved by various non-linear models. Experiments on a wide range of data sets validate the proposed model for the problem of interacting variables.

We present a new method for visual semantic segmentation of images at a low-dimensional plane with respect to non-convex parameters. Our method, named Deep Visual Semidefinite Network (DVS-NN), models the input vector as a non-convex function on the manifold, and extracts a non-convex function from the manifold to compute high-dimensional parametrized subgraphs for each quadrant. In this paper, an adversarial training is performed with a low-dimensional parametric model and the discriminative information is computed jointly from the manifold and the parametric model. The learned semantic segmentation aims at obtaining more precise parameters, and the discriminative information can be decoded for a lower-dimensional space. Experiments on three benchmark datasets demonstrate the potential of our method in terms of image segmentation performance on different semantic segmentations tasks.

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Learning Non-linear Structure from High-Order Interactions in Graphical Models

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  • Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning

    Adversarial-inspired Semi-supervised Adversarial Image SegmentationWe present a new method for visual semantic segmentation of images at a low-dimensional plane with respect to non-convex parameters. Our method, named Deep Visual Semidefinite Network (DVS-NN), models the input vector as a non-convex function on the manifold, and extracts a non-convex function from the manifold to compute high-dimensional parametrized subgraphs for each quadrant. In this paper, an adversarial training is performed with a low-dimensional parametric model and the discriminative information is computed jointly from the manifold and the parametric model. The learned semantic segmentation aims at obtaining more precise parameters, and the discriminative information can be decoded for a lower-dimensional space. Experiments on three benchmark datasets demonstrate the potential of our method in terms of image segmentation performance on different semantic segmentations tasks.


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