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I think if you want to resize and reduce your matrix size, you can use one of the dimension reduction techniques. Here there is a link that may be helpful to you.


In Machine Learning "embedding" means taking some set of raw inputs (like natural language tokens in NLP or image patches in your example) and converting them to vectors somehow. The embeddings usually have some interesting dot-product structure between vectors (like in word2vec for example). The Transformer machinery then uses this embedding in ...

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