Theano: a powerful compressive n-gram generator – Theano: a powerful compressive n-gram generator is a natural extension of Theano to the domain of language. In this paper, We present and evaluate a novel approach for learning a new language by exploiting a variety of techniques of the Theano, including the use of the N-gram. This approach is also motivated on the grounds that we can learn a language using an N-gram generator without any training data and with no knowledge of the N-gram generator’s vocabulary size. We develop and evaluate this approach using synthetic language and syntactic resources from various scientific institutions and demonstrate how it improves performance over Theano, on both synthetic and real N-gram generation.
We present a new method for text mining that utilizes a combination of multiple semantic and syntactic distance measures to train an intelligent algorithm that is able to extract and recognize the semantic, syntactic and non-syntactic information from a corpus. We evaluate our approach using several datasets and compare the performance of the proposed method. We show that our method performs better than state-of-the-art word segmentation approaches, and that it achieves the best accuracy for recognizing semantic and syntactic information in a corpus.
We propose a novel novel non-negative matrix factorization algorithm based on sparse representation of a vector space. Our method outperforms the state-of-the-art in terms of solving the optimization problem by a significant margin. We present a comprehensive comparison between different approaches and demonstrate an improvement in the prediction performance for the supervised classification problem of MML.
Distributed Optimistic Sampling for Population Genetics
Theano: a powerful compressive n-gram generator
Learning a Hierarchical Bayesian Network Model for Automated Speech Recognition
Toward Accurate Text Recognition via Transfer LearningWe present a new method for text mining that utilizes a combination of multiple semantic and syntactic distance measures to train an intelligent algorithm that is able to extract and recognize the semantic, syntactic and non-syntactic information from a corpus. We evaluate our approach using several datasets and compare the performance of the proposed method. We show that our method performs better than state-of-the-art word segmentation approaches, and that it achieves the best accuracy for recognizing semantic and syntactic information in a corpus.
We propose a novel novel non-negative matrix factorization algorithm based on sparse representation of a vector space. Our method outperforms the state-of-the-art in terms of solving the optimization problem by a significant margin. We present a comprehensive comparison between different approaches and demonstrate an improvement in the prediction performance for the supervised classification problem of MML.
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