Adaptive Stochastic Learning


Adaptive Stochastic Learning – We present a novel approach, based on an extended version of the recently proposed deep convolutional neural networks (CNNs) learning from input images. At a higher level of abstraction, we use two iterative steps for learning a global feature for each image. When the feature is a high-dimensional feature, the CNNs will learn a sparse representation of the feature with respect to the input image. When the feature is a low-dimensional feature, the CNNs will learn a low-dimensional representation from the input image. This approach allows for both direct and indirect feedback loops where the input is the source domain and the outputs of a CNN are the output domain. The proposed approach is demonstrated on MNIST and ImageNet datasets. The method achieved comparable performance to state-of-the-art CNNs by only training on three datasets and outperforming the state-of-the-art CNNs on two of them by a large margin.

This paper proposes a generalization learning method called a generative model based model (GMM) for detecting human poses. In this paper, we propose a framework designed to learn a model that learns a generative model for a given human pose. In particular, we propose a novel hierarchical method of learning a hierarchical model, based on a hierarchical generative model, that maps the human pose into the hierarchical generative model. We show that the hierarchical generative model is very efficient in the presence of pose-wise interactions. We also show that it can learn meaningful human pose features and we propose a generic framework based on this framework to learn a model for a given human pose. We discuss the advantages of our generative model and the trade-off between generative models in terms of their performance in detecting human poses and inferring meaningful human poses. The proposed approach is evaluated and compared on the CIFAR-10 and ImageNet datasets, showing that the proposed approach leads to promising performance, and also outperforms state-of-the-art approaches.

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Adaptive Stochastic Learning

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  • Deep Learning with a Recurrent Graph Laplacian: From Linear Regression to Sparse Tensor Recovery

    Stable CNN Features: Learning to Generate True Color FeaturesThis paper proposes a generalization learning method called a generative model based model (GMM) for detecting human poses. In this paper, we propose a framework designed to learn a model that learns a generative model for a given human pose. In particular, we propose a novel hierarchical method of learning a hierarchical model, based on a hierarchical generative model, that maps the human pose into the hierarchical generative model. We show that the hierarchical generative model is very efficient in the presence of pose-wise interactions. We also show that it can learn meaningful human pose features and we propose a generic framework based on this framework to learn a model for a given human pose. We discuss the advantages of our generative model and the trade-off between generative models in terms of their performance in detecting human poses and inferring meaningful human poses. The proposed approach is evaluated and compared on the CIFAR-10 and ImageNet datasets, showing that the proposed approach leads to promising performance, and also outperforms state-of-the-art approaches.


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