Adaptive Neighbors and Neighbors by Nonconvex Surrogate Optimization


Adaptive Neighbors and Neighbors by Nonconvex Surrogate Optimization – This work addresses a question that has received much interest in recent years: how to use multiple independent variables to find the optimal learning policy for each variable? Unfortunately, it is difficult to generalize the solution to this problem to any fixed model given only the data set. Such problems are difficult to solve on a practical level. In this paper we present an algorithm for learning to efficiently solve problems with multiple independent variables, such as learning from a single continuous variable, learning to predict the future, and learning to learn to predict the past. Our algorithm is applicable to any continuous variable model, including a random variable. We demonstrate that our algorithm can be applied to a wide class of continuous variables, for example: a multilevel function, a family of random variables such as a Markov random field, and a model-free continuous variable model, which learns to predict future outcomes with a continuous variable. Our algorithm is much faster than the traditional multilevel algorithms. We also show that it is well optimized for learning to predict the past with multiple independent variables.

We propose a novel approach for clustering the information content of data in a nonlinear manner. The goal of the proposed architecture is to reduce the dimension of the input data set by at least an order of magnitude. The proposed architecture is able to solve the clustering problem on a low-rank rank-1 manifold, while keeping the underlying Euclidean distance of each label and the corresponding density of the data. The architecture is a nonlinear, iterative model and can be used to efficiently estimate the clusters within a data set. The proposed learning scheme is computationally efficient and is well-suited for practical clustering tasks, such as image retrieval, clustering or data-to-data transformation, where the model optimises the clustering performance. The experimental results on a variety of datasets are presented to illustrate the superiority of the proposed approach by comparing to state of the art methods.

On the Number of Training Variants of Deep Neural Networks

Stochastic Optimization for Deep Neural Networks

Adaptive Neighbors and Neighbors by Nonconvex Surrogate Optimization

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  • Single-Shot Recognition with Deep Priors

    Fast Convergence of Low-rank Determinantal Point ProcessesWe propose a novel approach for clustering the information content of data in a nonlinear manner. The goal of the proposed architecture is to reduce the dimension of the input data set by at least an order of magnitude. The proposed architecture is able to solve the clustering problem on a low-rank rank-1 manifold, while keeping the underlying Euclidean distance of each label and the corresponding density of the data. The architecture is a nonlinear, iterative model and can be used to efficiently estimate the clusters within a data set. The proposed learning scheme is computationally efficient and is well-suited for practical clustering tasks, such as image retrieval, clustering or data-to-data transformation, where the model optimises the clustering performance. The experimental results on a variety of datasets are presented to illustrate the superiority of the proposed approach by comparing to state of the art methods.


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