Faster Training of Neural Networks via Convex Optimization


Faster Training of Neural Networks via Convex Optimization – We provide two new techniques for learning the linear objective function for a class of nonlinear sparse features (in particular, sparse-feature training), which is an extension of the sparse-feature classification technique with a special formulation in which the features are sampled from an infinite set of sparse-feature classes. We prove that this formulation and the algorithms in its extension can be extended to the sparse-feature classification problem for latent variable models, when each feature of the problem is composed of two independent variables (the feature vector and the latent variable) with a similar distribution. The resulting algorithms are shown to be of the same complexity as those used in standard sparse-feature learning. Experimental results on the MNIST dataset show that our extensions to the sparse-feature classification problem outperform the standard sparse-feature classification on both the empirical and theoretical evaluations.

This paper presents a practical approach to the solution of nonconvex problems of matrix completion. We show an efficient way to solve these problems in the form of a greedy search of the matrix for all possible solutions. Our method can be used to solve multi-armed bandit problems, where the problem-solving is restricted to solve the case of a large number of arms. The algorithm is based on greedy exploration of the matrix for a subset of an unknown objective. Our algorithm is based on the notion of optimal search under the general-exploring model. The algorithm is evaluated on a real-world dataset of large-scale data sets of various types.

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Faster Training of Neural Networks via Convex Optimization

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  • Identifying Events from Multiscale Sequences with a Bagged Entropic Markov Model

    Learning to see through the MatrixThis paper presents a practical approach to the solution of nonconvex problems of matrix completion. We show an efficient way to solve these problems in the form of a greedy search of the matrix for all possible solutions. Our method can be used to solve multi-armed bandit problems, where the problem-solving is restricted to solve the case of a large number of arms. The algorithm is based on greedy exploration of the matrix for a subset of an unknown objective. Our algorithm is based on the notion of optimal search under the general-exploring model. The algorithm is evaluated on a real-world dataset of large-scale data sets of various types.


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