Robust Spherical Sentence Encoding


Robust Spherical Sentence Encoding – A neural network learns to capture linguistic and syntactic information from the data. In this paper, we develop an end-to-end learning pipeline that directly models the linguistic and syntactic cues in a language and learns to capture them in a predictive framework. Based on this framework, we propose an efficient and natural approach to learn natural language, which can be used for different types of language. We illustrate our approach on COCO, a dataset of English sentences.

This work presents two approaches for solving the stochastic optimization problem. This solution is a general representation of the convex optimization problem, and is used to solve the recently-proposed SVM. The problem has been studied extensively, and the results of this study can be compared with previous work.

Bayes-Ball and Fisher Discriminant Analysis

A Comparison of Several Convex Optimization Algorithms

Robust Spherical Sentence Encoding

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  • A unified approach to modeling ontologies, networks and agents

    Convex Penalized Kernel SVMThis work presents two approaches for solving the stochastic optimization problem. This solution is a general representation of the convex optimization problem, and is used to solve the recently-proposed SVM. The problem has been studied extensively, and the results of this study can be compared with previous work.


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