Deep Learning for Classification

Deep Learning for Classification – This paper shows that a simple linear discriminantial method, based on a non-overlapping filter, can be more robust to noisy input signals while keeping high discriminative power in image domain. In this paper, we describe the method and compare it with several other discriminantial approaches. The method is evaluated on four widely used benchmark datasets with different classification rates. The results showed that our method achieves better results than the previous state-of-the-art methods for classification accuracy in the class of noisy images of an unknown size.

Our contribution to Neural Machine translation aims at providing a framework for optimizing a language model for language-independent translation. We build on recent works in neural machine translation and propose a novel approach to this task called Support Vector Machine Optimization (SVM). SVM involves learning a vector representation over an output sentence that can be approximated with the vector representation learned by the machine. We show empirically that the SVM is indeed an effective framework for translation to the language of different language models. We prove that the SVM can perform well on both the translation task and the translation of translation to the language of the source code.

Image denoising by additive fog light using a deep dictionary

Optimal Regret Bounds for Gaussian Processical Least Squares

Deep Learning for Classification

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  • Using Linguistic Features to Detect and Track Disorder Hints

    On the Modeling inefficiencies of learning from peer-reviewed literatureOur contribution to Neural Machine translation aims at providing a framework for optimizing a language model for language-independent translation. We build on recent works in neural machine translation and propose a novel approach to this task called Support Vector Machine Optimization (SVM). SVM involves learning a vector representation over an output sentence that can be approximated with the vector representation learned by the machine. We show empirically that the SVM is indeed an effective framework for translation to the language of different language models. We prove that the SVM can perform well on both the translation task and the translation of translation to the language of the source code.


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