Graphical learning via convex optimization: Two-layer random compositionality


Graphical learning via convex optimization: Two-layer random compositionality – Generative adversarial networks (GANs) have been widely employed in many applications. In this work we propose a new GAN framework for generating realistic and realistic images. The framework, dubbed ROGNN, has been implemented in two parts. First, a new generation of images called ROGNN-generated images is generated using a novel type of dynamic graph. Second, a neural network that learns a visual representation of images is trained to predict the features used for generating the images. We demonstrate the effectiveness of the approach on three real-world applications where our framework outperforms state-of-the-art deep learning approaches on the first two. On the third use case, we show that our GAN framework is able to generate realistic images, using the same parameters of the generated images as well as the same feature representation. The proposed framework achieves competitive performance on two real-world datasets.

We present a novel architecture for facial expression recognition. This approach, called Global Facial Representation Model (GF-RMM), can be used to improve image and facial data representation and data processing. The proposed GF-RMM framework is built to represent facial features that are common in the human face by extracting a global representation of the given face, which is then used to obtain facial features. Moreover, to improve the accuracy, this approach uses a two-stream approach based on multiple representations learned locally based on a facial feature representation. The approach is compared with several related methods on the MNIST dataset and found that GF-RMM is an improvement over several methods such as the standard approach of generating facial features for facial features, to use the global representations to achieve better accuracy.

Parsimonious Topic Modeling for Medical Concepts and Part-of-Speech Tagging

Flexible Clustering and Efficient Data Generation for Fast and Accurate Image Classification

Graphical learning via convex optimization: Two-layer random compositionality

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  • An efficient non-weight preserving algorithm for Bayesian nonparametric estimation

    A Comprehensive Survey on Appearance-Based Facial Expressions, Face Typing, and Appearance-Based Facial FeaturesWe present a novel architecture for facial expression recognition. This approach, called Global Facial Representation Model (GF-RMM), can be used to improve image and facial data representation and data processing. The proposed GF-RMM framework is built to represent facial features that are common in the human face by extracting a global representation of the given face, which is then used to obtain facial features. Moreover, to improve the accuracy, this approach uses a two-stream approach based on multiple representations learned locally based on a facial feature representation. The approach is compared with several related methods on the MNIST dataset and found that GF-RMM is an improvement over several methods such as the standard approach of generating facial features for facial features, to use the global representations to achieve better accuracy.


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