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Learning from set-structured data is an essential problem with many applications in machine learning and computer vision.
Multidimensional binary search trees used for associative searching
Jon Louis Bentley · 1975
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Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Similarity estimation techniques from rounding algorithms
Moses S Charikar · 2002
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Locality-sensitive hashing scheme based on p-stable distributions
Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni · 2004
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Probability product kernels
Tony Jebara, Risi Kondor, and Andrew Howard · 2004
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Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Alexandr Andoni and Piotr Indyk · 2006
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A kernel method for the two-sample-problem
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex Smola · 2006
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On building fast kd-trees for ray tracing, and on doing that in o (n log n)
Ingo Wald and Vlastimil Havran · 2006
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Object retrieval with large vocabularies and fast spatial matching
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2007
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In defense of nearest-neighbor based image classification
Oren Boiman, Eli Shechtman, and Michal Irani · 2008
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Nearest-neighbor methods in learning and vision
Gregory Shakhnarovich, Trevor Darrell, and Piotr Indyk · 2008
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Optimal transport: old and new
Cédric Villani · 2008
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Kernelized locality-sensitive hashing for scalable image search
Brian Kulis and Kristen Grauman · 2009
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Aggregating local descriptors into a compact image representation
Hervé Jégou, Matthijs Douze, Cordelia Schmid, and Patrick Pérez · 2010
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Nonparametric divergence estimation with applications to machine learning on distributions
Barnabás Póczos, Liang Xiong, and Jeff Schneider · 2011
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Learning from distributions via support measure machines
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, and Bernhard Schölkopf · 2012
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Nonparametric estimation of conditional information and divergences
Barnabás Póczos and Jeff Schneider · 2012
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Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2012
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All about vlad
Relja Arandjelovic and Andrew Zisserman · 2013
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Unidimensional and evolution methods for optimal transportation
Nicolas Bonnotte · 2013
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A linear optimal transportation framework for quantifying and visualizing variations in sets of images
Wei Wang, Dejan Slepčev, Saurav Basu, John A Ozolek, and Gustavo K Rohde · 2013
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Beyond locality-sensitive hashing
Alexandr Andoni, Piotr Indyk, Huy L Nguyen, and Ilya Razenshteyn · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Learning from point sets with observational bias
Liang Xiong and Jeff Schneider · 2014
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Optimal data-dependent hashing for approximate near neighbors
Alexandr Andoni and Ilya Razenshteyn · 2015
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Sliced and Radon Wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2016
Generalized sliced wasserstein distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, and Gustavo Rohde · 2019
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Sliced Wasserstein auto-encoders
Soheil Kolouri, Phillip E. Pope, Charles E. Martin, and Gustavo K. Rohde · 2019
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Sliced wasserstein discrepancy for unsupervised domain adaptation
Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, and Daniel Ulbricht · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
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Sliced-wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, and Fabian-Robert Stöter · 2019
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Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
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Cross-dimensional weighting for aggregated deep convolutional features
Yannis Kalantidis, Clayton Mellina, and Simon Osindero · 2016
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The Radon cumulative distribution transform and its application to image classification
Soheil Kolouri, Se Rim Park, and Gustavo K. Rohde · 2016
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Sliced-Wasserstein kernels for probability distributions
Soheil Kolouri, Yang Zou, and Gustavo K Rohde · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Antoine Liutkus, Umut Şimşekli, Szymon Majewski, Alain Durmus, and Fabian-Robert Stoter · 2019
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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
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Subspace robust wasserstein distances
François-Pierre Paty and Marco Cuturi · 2019
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Wasserstein weisfeiler-lehman graph kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, and Karsten Borgwardt · 2019
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On the limitations of representing functions on sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, and Michael A Osborne · 2019
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Wasserstein adversarial examples via projected sinkhorn iterations
Eric Wong, Frank Schmidt, and Zico Kolter · 2019
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Fspool: Learning set representations with featurewise sort pooling
Yan Zhang, Jonathon Hare, and Adam Prügel-Bennett · 2019
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Geometric dataset distances via optimal transport
David Alvarez Melis and Nicolo Fusi · 2020
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Point cloud mnist 2d, 2020
Cristian Garcia · 2020
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Locality sensitive hashing for set-queries, motivated by group recommendations
Haim Kaplan and Jay Tenenbaum · 2020
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Wasserstein smoothing: Certified robustness against wasserstein adversarial attacks
Alexander Levine and Soheil Feizi · 2020
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Caroline Moosmüller and Alexander Cloninger · 2020
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Statistical and topological properties of sliced probability divergences
Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, and Umut Şimşekli · 2020
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Deep cnns meet global covariance pooling: Better representation and generalization
Qilong Wang, Jiangtao Xie, Wangmeng Zuo, Lei Zhang, and Peihua Li · 2020
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Stronger and faster wasserstein adversarial attacks
Kaiwen Wu, Allen Wang, and Yaoliang Yu · 2020
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Fspool: Learning set representations with featurewise sort pooling
Yan Zhang, Jonathon Hare, and Adam Prügel-Bennett · 2020
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Wasserstein embedding for graph learning
Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, and Heiko Hoffmann · 2021
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A trainable optimal transport embedding for feature aggregation and its relationship to attention
Grégoire Mialon, Dexiong Chen, Alexandre d’Aspremont, and Julien Mairal · 2021
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Radon cumulative distribution transform subspace modeling for image classification
Mohammad Shifat-E-Rabbi, Xuwang Yin, Abu Hasnat Mohammad Rubaiyat, Shiying Li, Soheil Kolouri, Akram Aldroubi, Jonathan M Nichols, and Gustavo K Rohde · 2021
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