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Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data.
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Jerry L Hintze and Ray D Nelson · 1998
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Samuel Roweis and Lawrence Saul · 2000
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Stochastic neighbor embedding
Geoffrey E Hinton and Sam T Roweis · 2003
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Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Matplotlib: A 2D graphics environment
John D. Hunter · 2007
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Laurens van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
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Laurens van der Maaten · 2009
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SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien · 2014
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t-SNE visualization of CNN codes, 2014
Andrej Karpathy · 2014
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Laurens van der Maaten · 2014
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Alpha-beta divergences discover micro and macro structures in data
Karthik S Narayan, Ali Punjani, and Pieter Abbeel · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Visualizing large-scale and high-dimensional data
Jian Tang, Jingzhou Liu, Ming Zhang, and Qiaozhu Mei · 2016
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How to use t-SNE effectively
Martin Wattenberg, Fernanda Viégas, and Ian Johnson · 2016
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Leland McInnes, John Healy, and Steve Astels · 2017
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Hyunghoon Cho, Bonnie Berger, and Jian Peng · 2018
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Jiarui Ding, Anne Condon, and Sohrab P Shah · 2018
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Björn Barz and Joachim Denzler · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Array programming with NumPy
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Exploring simple Siamese representation learning
Xinlei Chen and Kaiming He · 2021
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UMAP: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Stable random projection: Lightweight, general-purpose dimensionality reduction for digitized libraries
Benjamin Schmidt · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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TriMap: Large-scale Dimensionality Reduction Using Triplets
Ehsan Amid and Manfred K. Warmuth · 2019
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Dimensionality reduction for visualizing single-cell data using UMAP
Etienne Becht, Leland McInnes, John Healy, Charles-Antoine Dutertre, Immanuel WH Kwok, Lai Guan Ng, Florent Ginhoux, and Evan W Newell · 2019
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UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts
Alex Diaz-Papkovich, Luke Anderson-Trocmé, Chief Ben-Eghan, and Simon Gravel · 2019
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On UMAP’s true loss function
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Pervasive label errors in test sets destabilize machine learning benchmarks
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Parametric umap embeddings for representation and semisupervised learning
Tim Sainburg, Leland McInnes, and Timothy Q Gentner · 2021
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Understanding how dimension reduction tools work: An empirical approach to deciphering t-SNE, UMAP, TriMap, and PaCMAP for data visualization
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Attraction-repulsion spectrum in neighbor embeddings
Jan Niklas Böhm, Philipp Berens, and Dmitry Kobak · 2022
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