Approximation Algorithms for the Logarithmic Solution of Linear Energies

Approximation Algorithms for the Logarithmic Solution of Linear Energies – In this paper, we present a method for solving a general-purpose energy minimization problem that is easy to solve on many levels, and hence far the most significant ones. The goal is to minimize a sum of the total of all non-uniformly Gaussian factors. We present a Bayesian approach which is capable of solving general-purpose energy minimization problems, and it is based on a non-convex generalization of the Dirichlet equation. We illustrate the use of this method on finite-dimensional continuous variable and non-stationary variables, showing that the proposed method can solve the problem with a state-of-the-art efficiency. The empirical results show that the proposed method is competitive with state-of-the-art methods.

Neural networks provide a powerful representation of abstract thought patterns and can be used to model biological systems, as has been observed by many other researchers. However, the network representation suffers from overfitting, which leads to the lack of discriminative representations given the input data. We propose a novel approach to perform neural network representation learning by leveraging sparse representations and a recently proposed learning algorithm to learn a sparse representation from a single input. Through a novel deep learning mechanism that explicitly incorporates the dimensionality of the input data, the network learns a classification objective to capture the learned model structure. Importantly, we demonstrate that the proposed approach outperforms some state-of-the-art classifiers in the task of human visual recognition.

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Approximation Algorithms for the Logarithmic Solution of Linear Energies

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  • Neural Architectures of Visual Attention

    Density Characterization of Human Poses In The Presence of Fisher Vectors and One-Class ClassifiersNeural networks provide a powerful representation of abstract thought patterns and can be used to model biological systems, as has been observed by many other researchers. However, the network representation suffers from overfitting, which leads to the lack of discriminative representations given the input data. We propose a novel approach to perform neural network representation learning by leveraging sparse representations and a recently proposed learning algorithm to learn a sparse representation from a single input. Through a novel deep learning mechanism that explicitly incorporates the dimensionality of the input data, the network learns a classification objective to capture the learned model structure. Importantly, we demonstrate that the proposed approach outperforms some state-of-the-art classifiers in the task of human visual recognition.


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