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Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set.
Visualizing data using t-SNE
L. Van der Maaten and G. Hinton · 2008
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Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages
R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma · 2013
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Label Partitioning For Sublinear Ranking
J. Weston, A. Makadia, and H. Yee · 2013
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Robust bloom filters for large multilabel classification tasks
M Cissé, N. Usunier, T. Artières, and P. Gallinari · 2013
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Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
P. S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck · 2013
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Distributed Representations of Words and Phrases and Their Compositionality
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean · 2013
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FastXML: A Fast, Accurate and Stable Tree-classifier for eXtreme Multi-label Learning
Y. Prabhu and M. Varma · 2014
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Fast Label Embeddings via Randomized Linear Algebra
P. Mineiro and N. Karampatziakis · 2015
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Sparse Local Embeddings for Extreme Multi-label Classification
K. Bhatia, H. Jain, P. Kar, M. Varma, and P. Jain · 2015
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Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking and Other Missing Label Applications
H. Jain, Y. Prabhu, and M. Varma · 2016
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Extreme F-measure Maximization using Sparse Probability Estimates
K. Jasinska, K. Dembczynski, R. Busa-Fekete, K. Pfannschmidt, T. Klerx, and E. Hullermeier · 2016
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PD-Sparse: A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
E. H. I. Yen, X. Huang, K. Zhong, P. Ravikumar, and I. S. Dhillon · 2016
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Robust Extreme Multi-label Learning
C. Xu, D. Tao, and C. Xu · 2016
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The Extreme Classification Repository: Multi-label Datasets & Code, 2016
K. Bhatia, K. Dahiya, H. Jain, P. Kar, A. Mittal, Y. Prabhu, and M. Varma · 2016
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DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification
R. Babbar and B. Schölkopf · 2017
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AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification
Y. Tagami · 2017
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PPDSparse: A Parallel Primal-Dual Sparse Method for Extreme Classification
E. H. I. Yen, X. Huang, W. Dai, P. Ravikumar, I. Dhillon, and E. Xing · 2017
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Label Filters for Large Scale Multilabel Classification
A. Niculescu-Mizil and E. Abbasnejad · 2017
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Bag of Tricks for Efficient Text Classification
A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov · 2017
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Deep Learning for Extreme Multi-label Text Classification
J. Liu, W. Chang, Y. Wu, and Y. Yang · 2017
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Smart mining for deep metric learning
B. Harwood, Kumar B.-V., G. Carneiro, I. Reid, and T. Drummond · 2017
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Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising
Y. Prabhu, A. Kag, S. Harsola, R. Agrawal, and M. Varma · 2018
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Extreme multi-label learning with label features for warm-start tagging, ranking and recommendation
Y. Prabhu, A. Kag, S. Gopinath, K. Dahiya, S. Harsola, R. Agrawal, and M. Varma · 2018
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CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
W. Siblini, P. Kuntz, and F. Meyer · 2018
Cited alongside, same era.
A no-regret generalization of hierarchical softmax to extreme multi-label classification
M. Wydmuch, K. Jasinska, M. Kuznetsov, R. Busa-Fekete, and K. Dembczynski · 2018
Cited alongside, same era.
Deep Extreme Multi-label Learning
W. Zhang, L. Wang, J. Yan, X. Wang, and H. Zha · 2018
Cited alongside, same era.
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
F. Faghri, D.-J. Fleet, J.-R. Kiros, and S. Fidler · 2018
Cited alongside, same era.
Rare Query Expansion Through Generative Adversarial Networks in Search Advertising
M. C. Lee, B. Gao, and R. Zhang · 2018
Cited alongside, same era.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
H. Ye, Z. Chen, D.-H. Wang, and B. D. Davison · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, Haoqi Fan, Yuxin W., S. Xie, and R. Girshick · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Sparse, Dense, and Attentional Representations for Text Retrieval
Y. Luan, J. Eisenstein, K. Toutanova, and M. Collins · 2020
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Transformers: State-of-the-Art Natural Language Processing
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. Rush · 2020
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T. K. R. Medini, Q. Huang, Y. Wang, V. Mohan, and A. Shrivastava · 2019
Cited alongside, same era.
AttentionXML: Extreme Multi-Label Text Classification with Multi-Label Attention Based Recurrent Neural Networks
R. You, S. Dai, Z. Zhang, H. Mamitsuka, and S. Zhu · 2019
Cited alongside, same era.
Slice: Scalable Linear Extreme Classifiers trained on 100 Million Labels for Related Searches
H. Jain, V. Balasubramanian, B. Chunduri, and M. Varma · 2019
Cited alongside, same era.
Extreme Multi-Label Legal Text Classification: A case study in EU Legislation
I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras, and I. Androutsopoulos · 2019
Cited alongside, same era.
A Modular Deep Learning Approach for Extreme Multi-label Text Classification
C. W. Chang, H. F. Yu, K. Zhong, Y. Yang, and I. S. Dhillon · 2019
Cited alongside, same era.
Data scarcity, robustness and extreme multi-label classification
R. Babbar and B. Schölkopf · 2019
Cited alongside, same era.
Learning for Tail Label Data: A Label-Specific Feature Approach
T. Wei, W. W. Tu, and Y. F. Li · 2019
Cited alongside, same era.
Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
A. Y. Malkov and D. A. Yashunin · 2020
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Large-Scale Training System for 100-Million Classification at Alibaba
L. Song, P. Pan, K. Zhao, H. Yang, Y. Chen, Y. Zhang, Y. Xu, and R. Jin · 2020
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Dense passage retrieval for open-domain question answering
V. Karpukhin, B. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-T. Yih · 2020
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Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization
K. Kawaguchi and H. Lu · 2020
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DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
K. Dahiya, D. Saini, A. Mittal, A. Shaw, K. Dave, A. Soni, H. Jain, S. Agarwal, and M. Varma · 2021
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DECAF: Deep Extreme Classification with Label Features
A. Mittal, K. Dahiya, S. Agrawal, D. Saini, S. Agarwal, P. Kar, and M. Varma · 2021
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SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels
K. Dahiya, A. Agarwal, D. Saini, K. Gururaj, J. Jiao, A. Singh, S. Agarwal, P. Kar, and M. Varma · 2021
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ECLARE: Extreme Classification with Label Graph Correlations
A. Mittal, N. Sachdeva, S. Agrawal, S. Agarwal, P. Kar, and M. Varma · 2021
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GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification
D. Saini, A.K. Jain, K. Dave, J. Jiao, A. Singh, R. Zhang, and M. Varma · 2021
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Fast multi-resolution transformer fine-tuning for extreme multi-label text classification
J. Zhang, W. C. Chang, H. F. Yu, and I. Dhillon · 2021
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LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification
T. Jiang, D. Wang, L. Sun, H. Yang, Z. Zhao, and F. Zhuang · 2021
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Embedding convolutions for short text extreme classification with millions of labels
S. Kharbanda, A. Banerjee, A. Palrecha, and R. Babbar · 2021
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
L. Xiong, C. Xiong, Y. Li, K.-F. Tang, J. Liu, P. Bennett, J. Ahmed, and A. Overwijk · 2021
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Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
S. Hofstätter, S.-C. Lin, J.-H. Yang, J. Lin, and A. Hanbury · 2021
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Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering, 2021
Y. Qu, Y. Ding, J. Liu, K. Liu, R. Ren, W. X. Zhao, D. Dong, H. Wu, and H. Wang · 2021
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Even the simplest baseline needs careful re-investigation: A case study on XML-CNN
S.-A. Chen, J.-J. Liu, T.-H. Yang, H.-T. Lin, and C.-J. Lin · 2022
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Multimodal extreme classification
A. Mittal, K. Dahiya, S. Malani, J. Ramaswamy, S. Kuruvilla, J. Ajmera, K. Chang, S. Agrawal, P. Kar, and M. Varma · 2022
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