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RelKD 2024: The Second International Workshop on Resource-Efficient Learning for Knowledge Discovery

Published: 24 August 2024 Publication History

Abstract

Modern machine learning techniques, particularly deep learning, have showcased remarkable efficacy across numerous knowledge discovery and data mining applications. However, the advancement of many of these methods is frequently impeded by resource constraint challenges in many scenarios, such as limited labeled data (data-level), small model size requirements in real-world computing platforms (model-level), and efficient mapping of the computations to heterogeneous target hardware (system-level). Addressing all these factors is crucial for effectively and efficiently deploying developed models across a broad spectrum of real-world systems, including large-scale social network analysis, recommendation systems, and real-time anomaly detection. Therefore, there is a critical need to develop efficient learning techniques to address the challenges posed by resource limitations, whether from data, model/algorithm, or system/hardware perspectives. The proposed international workshop on "<u>R</u>esource-<u>E</u>fficient <u>L</u>earning for <u>K</u>nowledge <u>D</u>iscovery (RelKD 2024)" will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities of resource-efficient learning.

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            cover image ACM Conferences
            KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
            August 2024
            6901 pages
            ISBN:9798400704901
            DOI:10.1145/3637528
            Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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            New York, NY, United States

            Publication History

            Published: 24 August 2024

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            1. knowledge discovery
            2. resource-efficient learning

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