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Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the query vector to find relevant documents.
Modern information retrieval , volume 463
Ricardo Baeza-Yates, Berthier Ribeiro-Neto, et al · 1999
Earlier work this paper cites.
Similarity estimation techniques from rounding algorithms
Moses S Charikar · 2002
Earlier work this paper cites.
Learning to hash with binary reconstructive embeddings
Brian Kulis and Trevor Darrell · 2009
Earlier work this paper cites.
Feature hashing for large scale multitask learning
Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg · 2009
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
Earlier work this paper cites.
Feature-aware label space dimension reduction for multi-label classification
Yao-Nan Chen and Hsuan-Tien Lin · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages
Rahul Agrawal, Archit Gupta, Yashoteja Prabhu, and Manik Varma · 2013
Earlier work this paper cites.
Label partitioning for sublinear ranking
Jason Weston, Ameesh Makadia, and Hector Yee · 2013
Earlier work this paper cites.
Efficient multi-label classification with many labels
Wei Bi and James Kwok · 2013
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Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning
Yashoteja Prabhu and Manik Varma · 2014
Earlier work this paper cites.
Extreme Classification Repository
Manik Varma · 2014
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Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips)
Anshumali Shrivastava and Ping Li · 2014
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Sparse local embeddings for extreme multi-label classification
Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, and Prateek Jain · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch · 2015
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Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications
Himanshu Jain, Yashoteja Prabhu, and Manik Varma · 2016
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Pd-sparse: A primal and dual sparse approach to extreme multiclass and multilabel classification
Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, and Inderjit Dhillon · 2016
Learning spread-out local feature descriptors
Xu Zhang, Felix X Yu, Sanjiv Kumar, and Shih-Fu Chang · 2017
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Order preserving hashing for approximate nearest neighbor search
Jianfeng Wang, Jingdong Wang, Nenghai Yu, and Shipeng Li · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing
Hamed Zamani, Mostafa Dehghani, W Bruce Croft, Erik Learned-Miller, and Jaap Kamps · 2018
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Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising
Yashoteja Prabhu, Anil Kag, Shrutendra Harsola, Rahul Agrawal, and Manik Varma · 2018
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Ppdsparse: A parallel primal-dual sparse method for extreme classification
Ian EH Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit Dhillon, and Eric Xing · 2017
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Annexml: Approximate nearest neighbor search for extreme multi-label classification
Yukihiro Tagami · 2017
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A survey on learning to hash
Jingdong Wang, Ting Zhang, Nicu Sebe, Heng Tao Shen, et al · 2017
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Search engines: Information retrieval in practice , volume 520
W Bruce Croft, Donald Metzler, and Trevor Strohman
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Glove: Global vectors for word representation
RichardSocher JeffreyPennington and ChristopherD Manning
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Semantic product search
Priyanka Nigam, Yiwei Song, Vijai Mohan, Vihan Lakshman, Weitian Ding, Ankit Shingavi, Choon Hui Teo, Hao Gu, and Bing Yin · 2019
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Deep learning recommendation model for personalization and recommendation systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G Azzolini, et al · 2019
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Breaking the glass ceiling for embedding-based classifiers for large output spaces
Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N Holtmann-Rice, Satyen Kale, Sashank Reddi, and Sanjiv Kumar · 2019
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Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products
Tharun Kumar Reddy Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, and Anshumali Shrivastava · 2019
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Slice: Scalable linear extreme classifiers trained on 100 million labels for related searches
Himanshu Jain, Venkatesh Balasubramanian, Bhanu Chunduri, and Manik Varma · 2019
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