Every year hundreds of papers are published on Machine learning/Deep Learning in various journals and conferences. Most of them are based on a single method to solve a problem while some might have methods cross-referencing. The current methods and models are limited in number when we restrict ourselves to selected research papers while some research papers do cross-reference other methods which are quite helpful and have a lot more application than alone. The question is how and where to find and learn different methods to solve a problem and learn about models from research papers.
In the graph each research paper is presented as one single method, for example, Autoencoder (AE), or separately, Autoencoder (AE) -> Encoder (ENCDR), Decoder (DCDR), That is, one model can consist of several elements that can be used separately.
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