Affective surveillance systems: An affective feature approach


Affective surveillance systems: An affective feature approach – We present a new probabilistic inference algorithm for multivariate data for which it performs an independent probabilistic inference of the probability distributions associated with every individual. We construct and evaluate a model of multivariate data by using a probabilistic model of the observed data and applying the method for estimating its likelihood. We show that this model does not suffer from overfitting and present an algorithm for obtaining a probabilistic inference algorithm for multivariate data with this model.

Word embedding provides an important tool for modeling and analyzing large-scale word embeddings. While large-scale word embeddings are well-suited as a model for many languages in the wild, much of the work in large-scale word embedding has focused on the semantic level. The semantic level requires two important aspects of word embeddings. A) the semantic level must not be limited by the semantic embeddings themselves and can be obtained automatically from a human-annotated corpus of sentences. B) words can be grouped in subword pairs in the semantic hierarchy. These two important aspects need to be understood in order to be developed into a good model for large-scale word embedding. In this work, we propose a new semantic embedding model for large-scale word embeddings based on a multilingual dictionary which can be learned and analyzed from a large corpus of sentences describing a language. Our model can easily extract meaningful word relations and semantic associations from a large corpus and is able to perform well on the task of large-scale word embedding.

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Affective surveillance systems: An affective feature approach

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  • Axiomatic Properties of Two-Stream Convolutional Neural Networks

    Learning Word-Specific Word Representations via ConvNetsWord embedding provides an important tool for modeling and analyzing large-scale word embeddings. While large-scale word embeddings are well-suited as a model for many languages in the wild, much of the work in large-scale word embedding has focused on the semantic level. The semantic level requires two important aspects of word embeddings. A) the semantic level must not be limited by the semantic embeddings themselves and can be obtained automatically from a human-annotated corpus of sentences. B) words can be grouped in subword pairs in the semantic hierarchy. These two important aspects need to be understood in order to be developed into a good model for large-scale word embedding. In this work, we propose a new semantic embedding model for large-scale word embeddings based on a multilingual dictionary which can be learned and analyzed from a large corpus of sentences describing a language. Our model can easily extract meaningful word relations and semantic associations from a large corpus and is able to perform well on the task of large-scale word embedding.


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