Additionally, a neural network may be able to extract more complicated relationships amongst the features of existing data sets than a simple covariance matrix could. Instead of forcing your data to come neatly in the form of a single distribution or a mixture of distributions, one can process data that comes in more structured forms such as images. The simplest is that one can expand the expressiveness of probabilistic models by incorporating a neural network. There are many benefits of merging a neural network and a probabilistic model. The first is: what is the benefit? The second: how does one do that? Two natural questions emerge when considering the notion of combining a neural network and a probabilistic model. However, with the recent inclusion of custom distributions, one can use a quick hack in order to turn pomegranate's models into deep models. Thus far, pomegranate has stuck to probabilistic models that are not coupled with a neural network. Another example is a deep mixture model, where expectation-maximization is used to train the model on unlabeled samples. These resulting probabilities are then treated as the likelihood function P(D|M) by the model, regularized using the transition matrix, and then re-normalized to get the posterior probabilities. ![]() For example, deep hidden Markov models (DHMMs) are models where the input to the neural network is some observation, such as an image, and the output is the state in the hidden Markov model that the observation belongs to. There have been mergers between neural networks and probabilistic models. These models are frequently applied to domains where there is a great deal of raw structured data, such as computer vision, where neighboring pixels are strongly correlated, and natural language processing, where words are organized and modified in specific ways to convey meaning. Contact: networks have become exceedingly popular recently due, in part, to their ability to achieve state-of-the-art performance on a variety of tasks without requiring complicated feature extraction pipelines.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |