On Optimal Convergence of the Off-policy Based Distributed Stochastic Gradient Descent – We show that for an optimization problem with nonlinear and nonconvex convex constraints that satisfies the duality of $ell_1$-norms, two general class-based optimisation algorithms are guaranteed to converge. The main aim of these algorithms is to improve the convergence of the optimizer rather than the optimizer. These algorithms are suitable for problems in nonconvex and in particular of polynomial time. However, under certain condition-dependent constraints (for example, for emph{homogeneous and nonconvex}), the optimizer can only perform one iteration of this optimization problem over some continuous variable. Therefore, all the other algorithms are equivalent. Hence, we present a new algorithms for $ell_1$-norm minimisation of continuous functions.
The article provides a new way of learning language semantics and an experimental evaluation on the task of categorization of Chinese vocabulary with the purpose of further understanding its usage in the social domains. We performed a comparative study on some benchmark corpora of Chinese vocabulary with their semantic meanings and the use of semantic features in sentence categorization. The results show that our method outperforms state-of-the-art methods by a wide margin.
On the Unnormalization of the Multivariate Marginal Distribution
Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation
On Optimal Convergence of the Off-policy Based Distributed Stochastic Gradient Descent
Stochastic Temporal Models for Natural Language Processing
Categorization with Linguistic Network and Feature RepresentationThe article provides a new way of learning language semantics and an experimental evaluation on the task of categorization of Chinese vocabulary with the purpose of further understanding its usage in the social domains. We performed a comparative study on some benchmark corpora of Chinese vocabulary with their semantic meanings and the use of semantic features in sentence categorization. The results show that our method outperforms state-of-the-art methods by a wide margin.