Non-parametric Inference for Mixed Graphical Models


Non-parametric Inference for Mixed Graphical Models – We propose an unsupervised algorithm to predict the location of a node in a graph by means of a hidden Markov model. We propose a method for estimating the location of a node using Gaussian Processes based on two types of prior knowledge: (1) the prior knowledge used to estimate the node and the posterior information to infer its posterior; (2) the posterior information used to estimate the node’s location using Markov networks, a general purpose model that assumes that the node’s location is local to the center of the graph. More specifically, by estimating the prior and posterior knowledge of a node with respect to a tree, we design a linear sparse model that considers the tree as a prior over nodes, and uses it in order to estimate the node’s position. Since the prior and posterior information for nodes are local to each other, the node’s location can be estimated in the non-parametric manner via the tree. We present experimental results showing that the proposed method outperforms the state-of-the-art methods on several benchmark datasets.

We present a semi-supervised learning (SRL) framework to learn to classify images of patients with sleep disorders. We compare our method (called SRL-D) with one proposed semi-supervised dataset called CT-LID, which was used to train it. SRL-D has been used to train a semi-supervised classifier, but is less successful at predicting sleepiness, compared to the supervised classifier on CT-LID. We present a new approach to achieve similar performance to state-of-the-art semi-supervised classification approaches.

Multi-view Graph Convolutional Neural Network

Efficient Estimation of Local Feature Distribution

Non-parametric Inference for Mixed Graphical Models

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    Poseidon: An Efficient Convolutional Neural Network for Automatic Detection of Severe Sleep ApneaWe present a semi-supervised learning (SRL) framework to learn to classify images of patients with sleep disorders. We compare our method (called SRL-D) with one proposed semi-supervised dataset called CT-LID, which was used to train it. SRL-D has been used to train a semi-supervised classifier, but is less successful at predicting sleepiness, compared to the supervised classifier on CT-LID. We present a new approach to achieve similar performance to state-of-the-art semi-supervised classification approaches.


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