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Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects.
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G. Tsoumakas, I. Katakis, and I. Vlahavas, “Effective and efficient multilabel classification in domains with large number of labels,” in Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD’08) , vol. 21, 2008, pp. 53–59
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P. Blouw and C. Eliasmith, “A neurally plausible encoding of word order information into a semantic vector space,” 35th Annual Conference of the Cognitive Science Society , vol. 35, pp. 1905–1910, 2013
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T. Bekolay, J. Bergstra, E. Hunsberger, T. DeWolf, T. Stewart, D. Rasmussen, X. Choo, A. Voelker, and C. Eliasmith, “Nengo: a python tool for building large-scale functional brain models,” Frontiers in Neuroinformatics , vol. 7, p. 48, 2014. [Online]. Available: https://www.frontiersin.org/article/10.3389/fninf.2013.00048
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W. Bi and J. Kwok, “Efficient multi-label classification with many labels,” in ICML , ser. Proceedings of Machine Learning Research, S. Dasgupta and D. McAllester, Eds., vol. 28, no. 3. Atlanta, Georgia, USA: PMLR, 2013, pp. 405–413. [Online]. Available: http://proceedings.mlr.press/v28/bi13.html
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J. Weston, A. Makadia, and H. Yee, “Label partitioning for sublinear ranking,” in ICML , ser. Proceedings of Machine Learning Research, S. Dasgupta and D. McAllester, Eds., vol. 28, no. 2. Atlanta, Georgia, USA: PMLR, 2013, pp. 181–189. [Online]. Available: http://proceedings.mlr.press/v28/weston13.html
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J. McAuley and J. Leskovec, “Hidden factors and hidden topics: understanding rating dimensions with review text,” in Proceedings of the 7th ACM conference on Recommender systems , 2013, pp. 165–172
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T. C. Stewart and C. Eliasmith, “Large-scale synthesis of functional spiking neural circuits,” Proceedings of the IEEE , vol. 102, no. 5, pp. 881–898, 2014
2014
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Y. Prabhu and M. Varma, “Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’14. New York, NY, USA: Association for Computing Machinery, 2014, p. 263–272. [Online]. Available: https://doi.org/10.1145/2623330.2623651
2014
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H.-F. Yu, P. Jain, P. Kar, and I. S. Dhillon, “Large-scale multi-label learning with missing labels,” in Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 , ser. ICML’14. JMLR.org, 2014, p. I–593–I–601
W. Siblini, P. Kuntz, and F. Meyer, “Craftml, an efficient clustering-based random forest for extreme multi-label learning,” in ICML , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. Stockholmsmässan, Stockholm Sweden: PMLR, 2018, pp. 4664–4673. [Online]. Available: http://proceedings.mlr.press/v80/siblini18a.html
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M. Wydmuch, K. Jasinska, M. Kuznetsov, R. Busa-Fekete, and K. Dembczynski, “A no-regret generalization of hierarchical softmax to extreme multi-label classification,” in Advances in Neural Information Processing Systems , 2018, pp. 6355–6366
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E. P. Frady, D. Kleyko, and F. T. Sommer, “A theory of sequence indexing and working memory in recurrent neural networks,” Neural Computation , vol. 30, no. 6, pp. 1449–1513, 2018
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R. Pratap, “Efficient dimensionality reduction for sparse binary data,” in IEEE Big Data , 2018, pp. 152–157
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2014
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J. Leskovec and A. Krevl, “Snap datasets: Stanford large network dataset collection,” 2014
2014
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K. Bhatia, H. Jain, P. Kar, M. Varma, and P. Jain, “Sparse local embeddings for extreme multi-label classification,” in Advances in Neural Information Processing Systems 28 , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates, Inc., 2015, pp. 730–738. [Online]. Available: http://papers.nips.cc/paper/5969-sparse-local-embeddings-for-extreme-multi-label-classification.pdf
2015
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P. Neubert, S. Schubert, and P. Protzel, “Learning vector symbolic architectures for reactive robot behaviours,” in Proc. of Intl. Conf. on Intelligent Robots and Systems (IROS) Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics , 2016. [Online]. Available: https://www.tu-chemnitz.de/etit/proaut/publications/IROS2016{_}neubert.pdf
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P. Blouw, E. Solodkin, P. Thagard, and C. Eliasmith, “Concepts as semantic pointers: A framework and computational model,” Cognitive Science , vol. 40, no. 5, pp. 1128–1162, 7 2016. [Online]. Available: http://doi.wiley.com/10.1111/cogs.12265
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L. Serafini and A. D. Garcez, “Logic tensor networks: Deep learning and logical reasoning from data and knowledge,” CEUR Workshop Proceedings , vol. 1768, 2016
2016
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H. Jain, Y. Prabhu, and M. Varma, “Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 935–944. [Online]. Available: https://doi.org/10.1145/2939672.2939756
2016
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K. Bhatia, K. Dahiya, H. Jain, A. Mittal, Y. Prabhu, and M. Varma, “The extreme classification repository: Multi-label datasets and code,” 2016. [Online]. Available: http://manikvarma.org/downloads/XC/XMLRepository.html
2016
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M. Imani, D. Kong, A. Rahimi, and T. Rosing, “Voicehd: Hyperdimensional computing for efficient speech recognition,” in 2017 IEEE International Conference on Rebooting Computing (ICRC) . IEEE, nov 2017, pp. 1–8. [Online]. Available: http://ieeexplore.ieee.org/document/8123650/
2017
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S. Liao and B. Yuan, “Circconv: A structured convolution with low complexity,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 7 2019, pp. 4287–4294. [Online]. Available: http://www.aaai.org/ojs/index.php/AAAI/article/view/4337
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A. Jalan and P. Kar, “Accelerating extreme classification via adaptive feature agglomeration,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence . California: International Joint Conferences on Artificial Intelligence Organization, 8 2019, pp. 2600–2606. [Online]. Available: https://www.ijcai.org/proceedings/2019/361
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H. Jain, V. Balasubramanian, B. Chunduri, and M. Varma, “Slice: Scalable linear extreme classifiers trained on 100 million labels for related searches,” in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining , ser. WSDM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 528–536. [Online]. Available: https://doi.org/10.1145/3289600.3290979
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T. K. R. Medini, Q. Huang, Y. Wang, V. Mohan, and A. Shrivastava, “Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d\textquotesingle Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/file/69cd21a0e0b7d5f05dc88a0be36950c7-Paper.pdf
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J. Gosmann and C. Eliasmith, “Vector-derived transformation binding: An improved binding operation for deep symbol-like processing in neural networks,” Neural Comput. , vol. 31, no. 5, pp. 849–869, 5 2019. [Online]. Available: https://doi.org/10.1162/neco_a_01179
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W.-C. Chang, H.-F. Yu, K. Zhong, Y. Yang, and I. S. Dhillon, “Taming pretrained transformers for extreme multi-label text classification,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , ser. KDD ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 3163–3171. [Online]. Available: https://doi.org/10.1145/3394486.3403368
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K. Dahiya, D. Saini, A. Mittal, A. Shaw, K. Dave, A. Soni, H. Jain, S. Agarwal, and M. Varma, “Deepxml: A deep extreme multi-label learning framework applied to short text documents,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining , ser. WSDM ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 31–39. [Online]. Available: https://doi.org/10.1145/3437963.3441810
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