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DimGrow: Memory-Efficient Field-level Embedding Dimension Search

Huang, Yihong et al.
arXiv (All) · Papers
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machine-learning
Key feature fields need bigger embedding dimensionality, others need smaller. This demands automated dimension allocation. Existing approaches, such as pruning or Neural Architecture Search (NAS), require training a memory-intensive SuperNet that enumerates all possible dimension combinations, which
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