Questions tagged [curse-of-dimensionality]

For questions related to the concept of "curse of dimensionality", which refers to the problem of an exponential increase in volume which occurs when adding extra dimensions to the Euclidean (or input) space. In machine learning and statistics, the curse of dimensionality implies that more data is required to achieve statistical significance, as the number of dimensions of the input increases. The expression was introduced by Richard Bellman in 1957.

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Is it feasible to train a DQN with thousands of input ports?

I designed a DQN architecture for some problem. The problem has a parameter $m$ as the number of clients. In my situation, $m$ is large, $m\in\{100,200,\ldots,1000\}$. For this situation, the number ...
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What are the purposes of autoencoders?

Autoencoders are neural networks that learn a compressed representation of the input in order to later reconstruct it, so they can be used for dimensionality reduction. They are composed of an encoder ...