Papers by Deepanshu Gupta

1 papers
EELBERT: Tiny Models through Dynamic Embeddings (2023.emnlp-industry)

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Challenge: Empirical evaluation shows that the input embedding layer occupies a large portion of the model size.
Approach: They propose an approach for compression of transformer-based models with minimal impact on downstream tasks by replacing the input embedding layer with dynamic embeddable computations.
Outcome: Empirical evaluation shows that the proposed model is 15x smaller (1.2 MB) compared to the traditional model.

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