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Found 3 papers, 1 papers with code
Neural Network Compression using Binarization and Few Full-Precision Weights
Quantization and pruning are two effective Deep Neural Networks model compression methods.
Learning Early Exit Strategies for Additive Ranking Ensembles
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees.
Query-level Early Exit for Additive Learning-to-Rank Ensembles
In this paper, we investigate the novel problem of \textit{query-level early exiting}, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble.
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