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Embedding vs Latent Space Due to Machine Learning's recent and rapid renaissance, and the fact that it draws from many distinct areas of mathematics, statistics, and computer science, it often has a number of different terms for the same or similar concepts. "Latent space" and "embedding" both refer to an (often lower-dimensional) ...


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When it comes to normal layman terms "latent space" means it cannot be accessed, thus we have no direct control over it. We can only manipulate it indirectly, while "Embeddings" can be obtained directly. We can use deterministic operations or transformations to convert an object into its corresponding embedding space. There is no marked difference between ...


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The purpose of the input network is to embed the input tuple into a state/task representation, that can then be fed into the RNN hidden state at each time step. $(o^a_t,m^a′_{t−1},u^a_{t−1},a)$ (input) $\rightarrow$ input network (embedding) $\rightarrow$ $z_t$ (task representation) According to to section 6.1 of the paper, the input is a tuple represented ...


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Convert them into numbers (using one-hot vectors or direct numerical representations) and then concatenate them. Then, you can pass them through the Dense layer.


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Embedding is the process of representing data (from a source domain) in a new (or target) domain. Usually, the source domain is discrete, and the target domain is continuous. For example, embedding words into the continuous vector space can be done by the word2vec method. The main reason behind using the embedding is doing meaningful mathematical ...


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The expression "latent space" explicitly indicates that the space is associated with the mathematical concept of an hidden (or latent) variable, which cannot be observed directly, but only indirectly. The expression "embedding space" refers to a vector space that represents an original space of inputs (e.g. images or words). For example, in the case of "...


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