Learning the Interpretability of Word Embeddings


Learning the Interpretability of Word Embeddings – In this work, we propose to use the term `knowledgeable embedding’ to refer to knowledge or data that can be obtained from multiple sources, each of them being a product or a function of the two. Information can be extracted from multiple sources in a unified manner and given as a product of these. To overcome the computational difficulty (theoretical complexity, computational cost) of computing different embeddings in one image for different embeddings, we propose to use the term multi-embedding embedding (MIM) with the use of the concept of multi-embeddings. More precisely, to maximize the computational cost of MIM, we develop a method that minimizes the computational cost of MIM. The method is built to compute embeddings by using an embedding function, which is a function of a subset of embeddings. To the best of our knowledge, this is the first non-linear procedure to compute multi-embeddings for a given embeddings. The proposed approach is validated on two datasets.

Prostate diseases are a growing trend in modern life, with the emergence of many new types of diseases to be diagnosed and treated with. The major problem of identifying these disease-causing factors (eg, prostatic hyperplasia) is to identify the cause of the disease. In this paper, we propose a novel model to improve the predictive performance of the prostate cancer prognosis. This model combines two approaches: (i) a probabilistic model to analyze prostate cancer prognosis and (ii) synthetic prostate cancer prognosis model which is based on quantitative prostate cancer disease predictive data. The model is trained on a clinical prostate cancer histological data and a clinical prognostic data in a realistic simulation environment. The proposed method was validated by extensive simulation experiments on different clinical prostate diseases datasets. The performance of this model has been evaluated on a simulated clinical prostate cancer prognosis dataset and on a real clinical prostate cancer prognosis dataset.

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Learning the Interpretability of Word Embeddings

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  • Visual Tracking via Joint Hierarchical Classification

    A New Prostate Genomics Model Based on Pseudo Prostate Cancer Genomics Model and Weighted as Graph CodingProstate diseases are a growing trend in modern life, with the emergence of many new types of diseases to be diagnosed and treated with. The major problem of identifying these disease-causing factors (eg, prostatic hyperplasia) is to identify the cause of the disease. In this paper, we propose a novel model to improve the predictive performance of the prostate cancer prognosis. This model combines two approaches: (i) a probabilistic model to analyze prostate cancer prognosis and (ii) synthetic prostate cancer prognosis model which is based on quantitative prostate cancer disease predictive data. The model is trained on a clinical prostate cancer histological data and a clinical prognostic data in a realistic simulation environment. The proposed method was validated by extensive simulation experiments on different clinical prostate diseases datasets. The performance of this model has been evaluated on a simulated clinical prostate cancer prognosis dataset and on a real clinical prostate cancer prognosis dataset.


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