تقرير
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
| العنوان: | ELIAS: End-to-End Learning to Index and Search in Large Output Spaces |
|---|---|
| المؤلفون: | Gupta, Nilesh, Chen, Patrick H., Yu, Hsiang-Fu, Hsieh, Cho-Jui, Dhillon, Inderjit S |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Information Retrieval |
| الوصف: | Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search. Existing methods initialize the tree index by clustering the label space into a few mutually exclusive clusters based on pre-defined features and keep it fixed throughout the training procedure. This approach results in a sub-optimal indexing structure over the label space and limits the search performance to the quality of choices made during the initialization of the index. In this paper, we propose a novel method ELIAS which relaxes the tree-based index to a specialized weighted graph-based index which is learned end-to-end with the final task objective. More specifically, ELIAS models the discrete cluster-to-label assignments in the existing tree-based index as soft learnable parameters that are learned jointly with the rest of the ML model. ELIAS achieves state-of-the-art performance on several large-scale extreme classification benchmarks with millions of labels. In particular, ELIAS can be up to 2.5% better at precision@1 and up to 4% better at recall@100 than existing XMC methods. A PyTorch implementation of ELIAS along with other resources is available at https://github.com/nilesh2797/ELIAS. Comment: 21 pages, 9 figures, NeurIPS 2022 camera-ready publication |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2210.08410 |
| رقم الأكسشن: | edsarx.2210.08410 |
| قاعدة البيانات: | arXiv |
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