Early Exit Strategies for Learning-to-Rank Cascades
Author:
Affiliation:
1. Department of Environmental Science, Informatics and Statistics, Ca’Foscari University of Venice, Venice, Italy
2. Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy
Funder
SoBigData++ European Union?s Horizon 2020 research and innovation programme
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10311562.pdf?arnumber=10311562
Reference34 articles.
1. Joint Optimization of Cascade Ranking Models
2. Greedy function approximation: A gradient boosting machine.
3. LeeBERT: Learned Early Exit for BERT with cross-level optimization
4. A Deep Look into neural ranking models for information retrieval
5. Fast Feature Selection for Learning to Rank
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1. ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information Retrieval;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10
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