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Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases.
Least squares quantization in pcm
Lloyd, S · 1982
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Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P. and Motwani, R · 1998
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Similarity estimation techniques from rounding algorithms
Charikar, M. S · 2002
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The random projection method , volume 65
Vempala, S. S · 2005
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Random projection trees and low dimensional manifolds
Dasgupta, S. and Freund, Y · 2008
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Optimal quantization and bit allocation for compressing large discriminative feature space transforms
Marcheret, E., Goel, V., and Olsen, P. A · 2009
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Performance of recommender algorithms on top-n recommendation tasks
Cremonesi, P., Koren, Y., and Turrin, R · 2010
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Large scale image annotation: learning to rank with joint word-image embeddings
Weston, J., Bengio, S., and Usunier, N · 2010
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Product quantization for nearest neighbor search
Jegou, H., Douze, M., and Schmid, C · 2011
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Fast, accurate detection of 100,000 object classes on a single machine: Technical supplement
Dean, T., Ruzon, M., Segal, M., Shlens, J., Vijayanarasimhan, S., and Yagnik, J · 2013
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Optimized product quantization
Ge, T., He, K., Ke, Q., and Sun, J · 2013
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Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval
Gong, Y., Lazebnik, S., Gordo, A., and Perronnin, F · 2013
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K-means hashing: An affinity-preserving quantization method for learning binary compact codes
He, K., Wen, F., and Sun, J · 2013
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Additive quantization for extreme vector compression
Babenko, A. and Lempitsky, V · 2014
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Scalable nearest neighbor algorithms for high dimensional data
Muja, M. and Lowe, D. G · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
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Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips)
Shrivastava, A. and Li, P · 2014
Cited alongside, same era.
Hashing for similarity search: A survey
Wang, J., Shen, H. T., Song, J., and Ji, J · 2014
Cited alongside, same era.
Composite quantization for approximate nearest neighbor search
Zhang, T., Du, C., and Wang, J · 2014
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Practical and optimal lsh for angular distance
Andoni, A., Indyk, P., Laarhoven, T., Razenshteyn, I., and Schmidt, L · 2015
Cited alongside, same era.
Sparse local embeddings for extreme multi-label classification
Bhatia, K., Jain, H., Kar, P., Varma, M., and Jain, P · 2015
Cited alongside, same era.
Hashing with binary autoencoders
Carreira-Perpinán, M. A. and Raziperchikolaei, R · 2015
Stochastic generative hashing
Dai, B., Guo, R., Kumar, S., He, N., and Song, L · 2017
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Billion-scale similarity search with gpus
Johnson, J., Douze, M., and Jégou, H · 2017
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Neural episodic control
Pritzel, A., Uria, B., Srinivasan, S., Badia, A. P., Vinyals, O., Hassabis, D., Wierstra, D., and Blundell, C · 2017
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Starspace: Embed all the things!
Wu, L., Fisch, A., Chopra, S., Adams, K., Bordes, A., and Weston, J · 2017
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Multiscale quantization for fast similarity search
Wu, X., Guo, R., Suresh, A. T., Kumar, S., Holtmann-Rice, D. N., Simcha, D., and Yu, F · 2017
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SPTAG: A library for fast approximate nearest neighbor search , 2018
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Cited alongside, same era.
Deep hashing for compact binary codes learning
Liong, V. E., Lu, J., Wang, G., Moulin, P., and Zhou, J · 2015
Cited alongside, same era.
On symmetric and asymmetric lshs for inner product search
Neyshabur, B. and Srebro, N · 2015
Cited alongside, same era.
Babenko, A., Arandjelović, R., and Lempitsky, V · 2016
Cited alongside, same era.
Quantization based fast inner product search
Guo, R., Kumar, S., Choromanski, K., and Simcha, D · 2016
Cited alongside, same era.
FANNG: Fast approximate nearest neighbour graphs
Harwood, B. and Drummond, T · 2016
Cited alongside, same era.
Malkov, Y. A. and Yashunin, D. A · 2016
Cited alongside, same era.
Chen, Q., Wang, H., Li, M., Ren, G., Li, S., Zhu, J., Li, J., Liu, C., Zhang, L., and Wang, J · 2018
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Lsq++: Lower running time and higher recall in multi-codebook quantization
Martinez, J., Zakhmi, S., Hoos, H. H., and Little, J. J · 2018
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Loss decomposition for fast learning in large output spaces
Yen, I. E.-H., Kale, S., Yu, F., Holtmann-Rice, D., Kumar, S., and Ravikumar, P · 2018
Later among the works it cites.
Ann-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms
Aumüller, M., Bernhardsson, E., and Faithfull, A · 2019
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Learning dense representations for entity retrieval
Gillick, D., Kulkarni, S., Lansing, L., Presta, A., Baldridge, J., Ie, E., and Garcia-Olano, D · 2019
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Random projections with asymmetric quantization
Li, X. and Li, P · 2019
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On the downstream performance of compressed word embeddings
May, A., Zhang, J., Dao, T., and Ré, C · 2019
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Unsupervised neural quantization for compressed-domain similarity search
Morozov, S. and Babenko, A · 2019
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Stochastic negative mining for learning with large output spaces
Reddi, S. J., Kale, S., Yu, F., Holtmann-Rice, D., Chen, J., and Kumar, S · 2019
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Spreading vectors for similarity search
Sablayrolles, A., Douze, M., Schmid, C., and Jégou, H · 2019
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