ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information Retrieval
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ReNeuIR at SIGIR 2023 : The Second Workshop on Reaching Efficiency in Neural Information Retrieval. / Bruch, Sebastian; Maistro, Maria; Mackenzie, Joel; Nardini, Franco Maria.
SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., 2023. p. 3456-3459.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - ReNeuIR at SIGIR 2023
T2 - 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023
AU - Bruch, Sebastian
AU - Maistro, Maria
AU - Mackenzie, Joel
AU - Nardini, Franco Maria
N1 - Publisher Copyright: © 2023 Copyright held by the owner/author(s).
PY - 2023
Y1 - 2023
N2 - Multifaceted, empirical evaluation of algorithmic ideas is one of the central pillars of Information Retrieval (IR) research. The IR community has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. As the community moves towards even more complex deep learning models, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time and space; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment alike. Examining algorithms and models through the lens of holistic efficiency requires the establishment of standards and principles, from defining relevant concepts, to designing metrics, to creating guidelines for making sense of the significance of new findings. The second iteration of the ReNeuIR workshop aims to bring the community together to debate these questions, with the express purpose of moving towards a common benchmarking framework for efficiency.
AB - Multifaceted, empirical evaluation of algorithmic ideas is one of the central pillars of Information Retrieval (IR) research. The IR community has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. As the community moves towards even more complex deep learning models, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time and space; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment alike. Examining algorithms and models through the lens of holistic efficiency requires the establishment of standards and principles, from defining relevant concepts, to designing metrics, to creating guidelines for making sense of the significance of new findings. The second iteration of the ReNeuIR workshop aims to bring the community together to debate these questions, with the express purpose of moving towards a common benchmarking framework for efficiency.
KW - algorithms
KW - efficiency
KW - neural IR
KW - ranking
KW - retrieval
KW - sustainable IR
U2 - 10.1145/3539618.3591922
DO - 10.1145/3539618.3591922
M3 - Article in proceedings
AN - SCOPUS:85168682674
SP - 3456
EP - 3459
BT - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc.
Y2 - 23 July 2023 through 27 July 2023
ER -
ID: 390183393