Can a well-chosen embedding size improve the generalization performance of a model?

Apr 8, 2023 ... The output dimension is mantained, since values coming from each attention head are concatenated and then projected with a matrix WO∈Rhdv×dmodel ... An embedding refers to the reduction of high-dimensional data, such as unstructured text, to a representation with fewer dimensions, such as a vector. In data science, the term dimension typically refers to a feature or attribute of the data. Higher-dimensional data in AI refers to datasets with many features ... Oct 1, 2024 ... I have a database on a Qdrant with a vector of 256 dimensions. The embedding was done using the “text-embedding-3-large” model with the dimensions parameter ... We present a complementary system, AdaEmbed, to reduce the size of embeddings needed for the same DLRM accuracy via in-training embedding pruning. Apr 3, 2024 ... The reason the size increases is due to storing the extra passages as nested fields as well as all the actual embedding representations ... Aug 26, 2021 ... For variables with many unique categories, it creates an unmanageable number of dimensions. Since each item is technically equidistant in vector ... Jul 7, 2021 ... One rule of thumb is to use the fourth root of the total number of unique categorical elements while another is that the embedding dimension should be ... Aug 17, 2023 ... Word2Vec can be used to create dense word embeddings that are vectors that have a fixed size, regardless of the size of the vocabulary. This ... Oct 17, 2023 ... We've uncovered the fascinating world of distributed representations, high-dimensional spaces, and emergent properties in this exploration of word embeddings.