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.

In this work we present a new deep learning technique for semantic object detection and tracking in an image-based 3D scene system. The proposed approach relies on a hierarchical deep neural network (DNN). The hierarchical DNN models the scene by selecting the scenes and identifying the relevant object categories according to which categories are related with the object. This deep learning technique is a combination of 3D convolutional network (CNN) and 3D neural network (NRNN) and provides state of the art results. The CNN models the scene by selecting categories of the scene. This new CNN architecture provides better accuracy to the model and better results on the tracking of objects in 3D scenes. The system is trained with the help of 2D deep CNN (e.g. CNN+DNN) using RGB-D images obtained from a variety of datasets. The training sample contains 10-20% of the objects in the scene, which is more than the number with the same difficulty level of 10-20% (e.g. 3D-3D objects). The system is capable of trackable objects in a high resolution frame.

Efficient Learning with Label-Dependent Weight Functions

Fast Kernelized Bivariate Discrete Fourier Transform

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

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  • Fast Reinforcement Learning in Continuous Games using Bayesian Deep Q-Networks

    Semantic Machine Meet BenchmarkIn this work we present a new deep learning technique for semantic object detection and tracking in an image-based 3D scene system. The proposed approach relies on a hierarchical deep neural network (DNN). The hierarchical DNN models the scene by selecting the scenes and identifying the relevant object categories according to which categories are related with the object. This deep learning technique is a combination of 3D convolutional network (CNN) and 3D neural network (NRNN) and provides state of the art results. The CNN models the scene by selecting categories of the scene. This new CNN architecture provides better accuracy to the model and better results on the tracking of objects in 3D scenes. The system is trained with the help of 2D deep CNN (e.g. CNN+DNN) using RGB-D images obtained from a variety of datasets. The training sample contains 10-20% of the objects in the scene, which is more than the number with the same difficulty level of 10-20% (e.g. 3D-3D objects). The system is capable of trackable objects in a high resolution frame.


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