Fetching the paper…
Reading the bibliography…
Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms.
Robert F Sproull, Refinements to nearest-neighbor searching ink-dimensional trees , Algorithmica 6
1991
Earlier work this paper cites.
Fan RK Chung, Laplacians of graphs and cheeger’s inequalities , Combinatorics, Paul Erdos is Eighty 2
1996
Earlier work this paper cites.
Mayank Bawa, Tyson Condie, and Prasanna Ganesan, Lsh forest: self-tuning indexes for similarity search , Proceedings of the 14th international conference on World Wide Web, ACM, 2005, pp. 651–660
2005
Earlier work this paper cites.
Lawrence Cayton and Sanjoy Dasgupta, A learning framework for nearest neighbor search , Advances in Neural Information Processing Systems, 2007, pp. 233–240
2007
Earlier work this paper cites.
Qin Lv, William Josephson, Zhe Wang, Moses Charikar, and Kai Li, Multi-probe lsh: efficient indexing for high-dimensional similarity search , Proceedings of the 33rd international conference on Very large data bases, VLDB Endowment, 2007, pp. 950–961
2007
Earlier work this paper cites.
Neeraj Kumar, Li Zhang, and Shree Nayar, What is a good nearest neighbors algorithm for finding similar patches in images? , European conference on computer vision, Springer, 2008, pp. 364–378
2008
Earlier work this paper cites.
Xavier Glorot and Yoshua Bengio, Understanding the difficulty of training deep feedforward neural networks , International Conference on Artificial Intelligence and Statistics, 2010, pp. 249–256
2010
Earlier work this paper cites.
Herve Jégou, Matthijs Douze, and Cordelia Schmid, Product quantization for nearest neighbor search , IEEE transactions on pattern analysis and machine intelligence 33
2011
Earlier work this paper cites.
Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, and Thomas S Huang, Learning to search efficiently in high dimensions , Advances in Neural Information Processing Systems, 2011, pp. 1710–1718
2011
Earlier work this paper cites.
Bahman Bahmani, Ashish Goel, and Rajendra Shinde, Efficient distributed locality sensitive hashing , Proceedings of the 21st ACM international conference on Information and knowledge management, ACM, 2012, pp. 2174–2178
2012
Earlier work this paper cites.
Artem Babenko and Victor Lempitsky, The inverted multi-index , Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 3069–3076
2012
Earlier work this paper cites.
Sanjoy Dasgupta and Kaushik Sinha, Randomized partition trees for exact nearest neighbor search , Conference on Learning Theory, 2013, pp. 317–337
2013
Earlier work this paper cites.
Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin, Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval , IEEE Transactions on Pattern Analysis and Machine Intelligence 35
2013
Earlier work this paper cites.
Parikshit Ram and Alexander Gray, Which space partitioning tree to use for search? , Advances in Neural Information Processing Systems, 2013, pp. 656–664
2013
Earlier work this paper cites.
Peter Sanders and Christian Schulz, Think Locally, Act Globally: Highly Balanced Graph Partitioning , Proceedings of the 12th International Symposium on Experimental Algorithms (SEA’13), LNCS, vol. 7933, Springer, 2013, pp. 164–175
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Jeffrey Pennington, Richard Socher, and Christopher Manning, Glove: Global vectors for word representation , Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 1532–1543
2014
Earlier work this paper cites.
Yifang Sun, Wei Wang, Jianbin Qin, Ying Zhang, and Xuemin Lin, Srs: solving c-approximate nearest neighbor queries in high dimensional euclidean space with a tiny index , Proceedings of the VLDB Endowment 8
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, and Ludwig Schmidt, Practical and optimal lsh for angular distance , Advances in Neural Information Processing Systems, 2015, pp. 1225–1233
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Chris Metzler, Ali Mousavi, and Richard Baraniuk, Learned d-amp: Principled neural network based compressive image recovery , Advances in Neural Information Processing Systems, 2017, pp. 1772–1783
2017
Later among the works it cites.
Y Ni, K Chu, and J Bradley, Detecting abuse at scale: Locality sensitive hashing at uber engineering , 2017
2017
Later among the works it cites.
Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N Holtmann-Rice, David Simcha, and Felix Yu, Multiscale quantization for fast similarity search , Advances in Neural Information Processing Systems, 2017, pp. 5745–5755
2017
Later among the works it cites.
Haoyu Zhang and Qin Zhang, Embedjoin: Efficient edit similarity joins via embeddings , Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2017, pp. 585–594
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Diederik Kingma and Jimmy Ba, Adam: A method for stochastic optimization , International Conference for Learning Representations, 2015
2015
Cited alongside, same era.
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger, From word embeddings to document distances , International Conference on Machine Learning, 2015, pp. 957–966
2015
Cited alongside, same era.
Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, and Jie Zhou, Deep hashing for compact binary codes learning , Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 2475–2483
2015
Cited alongside, same era.
Ali Mousavi, Ankit B Patel, and Richard G Baraniuk, A deep learning approach to structured signal recovery , Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on, IEEE, 2015, pp. 1336–1343
2015
Cited alongside, same era.
Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran Gözcü, Ilija Bogunovic, and Volkan Cevher, Learning-based compressive subsampling , IEEE Journal of Selected Topics in Signal Processing 10
2016
Cited alongside, same era.
Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang, Learning to hash for indexing big data - a survey , Proceedings of the IEEE 104
2016
Cited alongside, same era.
Martin Aumüller, Erik Bernhardsson, and Alexander Faithfull, Ann-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms , International Conference on Similarity Search and Applications, Springer, 2017, pp. 34–49
2017
Cited alongside, same era.
Alexandr Andoni, Thijs Laarhoven, Ilya Razenshteyn, and Erik Waingarten, Optimal hashing-based time-space trade-offs for approximate near neighbors , Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, 2017, pp. 47–66
2017
Cited alongside, same era.
2018
Later among the works it cites.
Alexandr Andoni, Assaf Naor, Aleksandar Nikolov, Ilya Razenshteyn, and Erik Waingarten, Data-dependent hashing via nonlinear spectral gaps , Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing (2018), 787–800
2018
Later among the works it cites.
Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik, Learning to branch , International Conference on Machine Learning, 2018
2018
Later among the works it cites.
Aditya Bhaskara and Maheshakya Wijewardena, Distributed clustering via lsh based data partitioning , International Conference on Machine Learning, 2018, pp. 569–578
2018
Later among the works it cites.
Tim Kraska, Alex Beutel, Ed H Chi, Jeffrey Dean, and Neoklis Polyzotis, The case for learned index structures , Proceedings of the 2018 International Conference on Management of Data, ACM, 2018, pp. 489–504
2018
Later among the works it cites.
Omid Keivani and Kaushik Sinha, Improved nearest neighbor search using auxiliary information and priority functions , International Conference on Machine Learning, 2018, pp. 2578–2586
2018
Later among the works it cites.
Thodoris Lykouris and Sergei Vassilvitskii, Competitive caching with machine learned advice , International Conference on Machine Learning, 2018
2018
Later among the works it cites.
Michael Mitzenmacher, A model for learned bloom filters and optimizing by sandwiching , Advances in Neural Information Processing Systems, 2018
2018
Later among the works it cites.
Yury A Malkov and Dmitry A Yashunin, Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs , IEEE transactions on pattern analysis and machine intelligence (2018)
2018
Later among the works it cites.
Manish Purohit, Zoya Svitkina, and Ravi Kumar, Improving online algorithms via ml predictions , Advances in Neural Information Processing Systems, 2018, pp. 9661–9670
2018
Later among the works it cites.
2019
Closest in time.
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, and Herve Jégou, Spreading vectors for similarity search , International Conference on Learning Representations, 2019
2019
Closest in time.