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Many unsupervised representation learning methods belong to the class of similarity learning models.
The fast gauss transform
Leslie Greengard and John Strain · 1991
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Mining association rules between sets of items in large databases
Rakesh Agrawal, Tomasz Imielinski, and Arun Swami · 1993
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Approximate nearest neighbors: Towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 2000
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An improved data stream summary: The count-min sketch and its applications
Graham Cormode and S. Muthukrishnan · 2004
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The netflix prize
James Bennett, Stan Lanning, and Netflix Netflix · 2007
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Optimized product quantization for approximate nearest neighbor search
T. Ge, K. He, Q. Ke, and J. Sun · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Recsys challenge 2015 and the yoochoose dataset
David Ben-Shimon, Alexander Tsikinovsky, Michael Friedmann, Bracha Shapira, Lior Rokach, and Johannes Hoerle · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk · 2016
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Updatable, accurate, diverse, and scalable recommendations for interactive applications
Bibek Paudel, Fabian Christoffel, Chris Newell, and Abraham Bernstein · 2016
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Neural attentive session-based recommendation
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma · 2017
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Inter-session modeling for session-based recommendation
Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, and Helge Langseth · 2017
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Unbiasing truncated backpropagation through time
Corentin Tallec and Yann Ollivier · 2017
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Hashing-based-estimators for kernel density in high dimensions
M. Charikar and P. Siminelakis · 2017
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Geometric mean of probability measures and geodesics of fisher information metric, 2017
Mitsuhiro Itoh and Hiroyasu Satoh · 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 · 2017
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Race: Sub-linear memory sketches for approximate near-neighbor search on streaming data, 2019
Benjamin Coleman, Anshumali Shrivastava, and Richard G. Baraniuk · 2019
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Wasserstein barycenter model ensembling, 2019
Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Cicero Dos Santos, and Tom Sercu · 2019
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Learning deep representations by mutual information estimation and maximization
Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Session-based recommendation with graph neural networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan · 2019
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Space and time efficient kernel density estimation in high dimensions
Arturs Backurs, Piotr Indyk, and Tal Wagner · 2019
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Iman Kamehkhosh, Dietmar Jannach, and Malte Ludewig · 2017
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Evaluation of session-based recommendation algorithms
Malte Ludewig and Dietmar Jannach · 2018
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nowplaying-rs: A new benchmark dataset for building context-aware music recommender systems
Asmita Poddar, Eva Zangerle, and yi-hsuan Yang · 2018
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A simple but hard-to-beat baseline for session-based recommendations
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, and Xiangnan He · 2018
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Context tree for adaptive session-based recommendation
Fei Mi and B. Faltings · 2018
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Representation learning with contrastive predictive coding, 2018
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Grossberger · 2018
Cited alongside, same era.
Zhiqiang Pan, Fei Cai, Yanxiang Ling, and Maarten de Rijke · 2020
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TAGNN: Target Attentive Graph Neural Networks for Session-Based Recommendation
Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan · 2020
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Handling Information Loss of Graph Neural Networks for Session-Based Recommendation
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Ripple walk training: A subgraph-based training framework for large and deep graph neural network
Jiyang Bai, Yuxiang Ren, and J. Zhang · 2020
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Sub-linear race sketches for approximate kernel density estimation on streaming data
Benjamin Coleman and Anshumali Shrivastava · 2020
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A mutual information maximization perspective of language representation learning
Lingpeng Kong, Cyprien de Masson d’Autume, Lei Yu, Wang Ling, Zihang Dai, and Dani Yogatama · 2020
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On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, and Mario Lucic · 2020
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Cleora: A simple, strong and scalable graph embedding scheme
Barbara Rychalska, Piotr Bąbel, Konrad Gołuchowski, Andrzej Michałowski, and Jacek Dąbrowski · 2020
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