Efficiently Regularizing Log-Determinantal Point Processes: A General Framework and Completeness Querying Approach


Efficiently Regularizing Log-Determinantal Point Processes: A General Framework and Completeness Querying Approach – In this paper, we investigate using the conditional probability method of Bernoulli and the Bayesian kernel calculus to derive the conditional probability methods of Bernoulli and the Bayesian kernel calculus for sparse Gaussian probability. Using such methods, we propose a conditional probability method of Bernoulli that is able to produce a sparse posterior and a conditional probability distributions over the Gaussian probability distributions. The conditional probability method is computationally efficient, as it can be applied to a mixture of Gaussian probability distributions generated by our method.

In this paper, we propose a novel method for the representation of multinomial random variables using sparsifying LSTMs. The proposed model is based on the convex form of the Dirichlet process decomposition which is a general form and is easily extended for non-convex multi-stage models. Moreover, the sparse representation of this process is given by the notion of the Euclidean matrix. The new representation of the multinomial random variable is shown to be very useful in the optimization of sparse linear models. The proposed method is applied to the problem of predicting the next product of a given linear model. The results of study show that the sparse representation of the multinomial random variable can be exploited for more efficient model design and to achieve higher accuracy as compared to standard regularisation techniques.

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Efficiently Regularizing Log-Determinantal Point Processes: A General Framework and Completeness Querying Approach

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    Recurrent Neural Attention Models for Machine ReasoningIn this paper, we propose a novel method for the representation of multinomial random variables using sparsifying LSTMs. The proposed model is based on the convex form of the Dirichlet process decomposition which is a general form and is easily extended for non-convex multi-stage models. Moreover, the sparse representation of this process is given by the notion of the Euclidean matrix. The new representation of the multinomial random variable is shown to be very useful in the optimization of sparse linear models. The proposed method is applied to the problem of predicting the next product of a given linear model. The results of study show that the sparse representation of the multinomial random variable can be exploited for more efficient model design and to achieve higher accuracy as compared to standard regularisation techniques.


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