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CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification.

, , , and . NeurIPS, (2022)

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Data scarcity, robustness and extreme multi-label classification, and . (2019)Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model., , , , , , , , and . LChange@ACL, page 68-77. Association for Computational Linguistics, (2022)Using ECCO-BERT and the Historical Thesaurus of English to Explore Concepts and Agency in Historical Writing Interpreting the Eighteenth-century Luxury Debate., , , , , , , , , and 3 other author(s). DH, (2023)On empirical tradeoffs in large scale hierarchical classification., , , and . CIKM, page 2299-2302. ACM, (2012)Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification., , and . CoRR, (2019)InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification., , , , and . SIGIR, page 760-769. ACM, (2023)Towards Memory-Efficient Training for Extremely Large Output Spaces - Learning with 670k Labels on a Single Commodity GPU., and . ECML/PKDD (3), volume 14171 of Lecture Notes in Computer Science, page 689-704. Springer, (2023)Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading., , , and . INFOCOM, page 854-863. IEEE, (2020)Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models., , and . CoRR, (2024)Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification., , and . ESANN, page 223-228. (2020)