Abstract:A common approach for compressing NLP networks is to encode the embedding layer as a matrix A ∈ R n×d , compute its rank-j approximation A j via SVD, and then factor A j into a pair of matrices that correspond to smaller fully-connected layers to replace the original embedding layer. Geometrically, the rows of A represent points in R d , and the rows of A j represent their projections onto the jdimensional subspace that minimizes the sum of squared distances ("errors") to the points. In practice, these rows of… Show more
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