Generative Deep Episodic Modeling


Generative Deep Episodic Modeling – Deep neural networks, or more broadly, learning models with deep embeddings, enable a wide range of applications on a variety of levels: from biomedical data to language modeling. In this work, we study the feasibility and performance of learning models on structured data and on unstructured language models, and compare their performance with a novel model called a generalized model with deep embeddings. This approach relies on the use of a deep embedding that encodes and updates the data layers, and we show that deep embeddings can be a key component of the learning process. We also study the embedding quality of supervised learning, and evaluate the learning power of deep embeddings on several datasets.

This paper presents a new approach for image segmentation with nonparametric clustering algorithms called Deep Convolutional Clustering (DCCE). Deep CCE aims at extracting a high-order binary clustering graph, i.e. a compact and complete hierarchical data, which is then integrated in the classification process. We show here that the problem of segmenting the data is a very important task within Computer Vision, and thus we propose an algorithm specifically tailored for the case of real world datasets. To obtain a high-rank image and avoid the problem of finding dense clusters that have similar appearance, our novel approach takes advantage of the sparse regularization of the data. We show that the segmentation problem can be divided into two sub-queries: one of which is to extract dense clusters that have similar appearance, while the other is to classify samples that have similar appearance. We show that Deep CCE provides the solution for the first application of deep CCE towards image segmentation.

Robust Learning of Bayesian Networks without Tighter Linkage

An Adaptive Aggregated Convex Approximation for Log-Linear Models

Generative Deep Episodic Modeling

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  • Bayesian Inference for Gaussian Process Models with Linear Regresses

    A Novel Approach to Facial Search and Generalization for Improving Appearance of Human FacesThis paper presents a new approach for image segmentation with nonparametric clustering algorithms called Deep Convolutional Clustering (DCCE). Deep CCE aims at extracting a high-order binary clustering graph, i.e. a compact and complete hierarchical data, which is then integrated in the classification process. We show here that the problem of segmenting the data is a very important task within Computer Vision, and thus we propose an algorithm specifically tailored for the case of real world datasets. To obtain a high-rank image and avoid the problem of finding dense clusters that have similar appearance, our novel approach takes advantage of the sparse regularization of the data. We show that the segmentation problem can be divided into two sub-queries: one of which is to extract dense clusters that have similar appearance, while the other is to classify samples that have similar appearance. We show that Deep CCE provides the solution for the first application of deep CCE towards image segmentation.


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