How do techniques like Singular Value Decomposition (SVD), neural embeddings (e.g., word2vec, node2vec), and transfer learning contribute to the creation of dense representations, and in what scenarios might one approach be preferred over another? Additionally, how do these considerations vary across different data modalities, such as text, images, audio, and graphs, and what role does domain-specific knowledge play in determining the optimal level of abstraction for a given task?
How do techniques like Singular Value Decomposition (SVD), neural embeddings (e.g., word2vec, node2vec), and transfer learning contribute to the creation of dense representations, and in what scenarios might one approach be preferred over another? Additionally, how do these considerations vary across different data modalities, such as text, images, audio, and graphs, and what role does domain-specific knowledge play in determining the optimal level of abstraction for a given task?