Fetching the paper…
Reading the bibliography…
High dimensional data is often assumed to be concentrated on or near a low-dimensional manifold.
Liii. on lines and planes of closest fit to systems of points in space
Karl Pearson F.R.S · 1901
Earlier work this paper cites.
The imbedding problem for riemannian manifolds
John Nash · 1956
Earlier work this paper cites.
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
Joseph B Kruskal · 1964
Earlier work this paper cites.
A nonlinear mapping for data structure analysis
John W Sammon · 1969
Earlier work this paper cites.
Level set approach to mean curvature flow in arbitrary codimension
Luigi Ambrosio and H Mete Soner · 1994
Earlier work this paper cites.
Columbia object image library (coil-20)
Sameer A Nene, Shree K Nayar, Hiroshi Murase, et al · 1996
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford · 2000
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
Earlier work this paper cites.
Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
David L Donoho and Carrie Grimes · 2003
Earlier work this paper cites.
Stochastic neighbor embedding
Geoffrey E Hinton and Sam T Roweis · 2003
Earlier work this paper cites.
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
Ronald R Coifman, Stephane Lafon, Ann B Lee, Mauro Maggioni, Boaz Nadler, Frederick Warner, and Steven W Zucker · 2005
Earlier work this paper cites.
Diffusion maps
Ronald R Coifman and Stéphane Lafon · 2006
Earlier work this paper cites.
Efficient learning of sparse representations with an energy-based model
Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, and Yann L Cun · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Cited alongside, same era.
Sparse feature learning for deep belief networks
Marc’Aurelio Ranzato, Y-Lan Boureau, and Yann L Cun · 2008
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Cited alongside, same era.
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
Cited alongside, same era.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Cited alongside, same era.
What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Later among the works it cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Later among the works it cites.
Infovae: Balancing learning and inference in variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 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…
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Cited alongside, same era.
Analyzing noise in autoencoders and deep networks
Ben Poole, Jascha Sohl-Dickstein, and Surya Ganguli · 2014
Cited alongside, same era.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
Cited alongside, same era.
Importance weighted autoencoders
Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov · 2016
Cited alongside, same era.
Leland McInnes, John Healy, and James Melville · 2018
Later among the works it cites.
Wasserstein auto-encoders
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schölkopf · 2018
Later among the works it cites.
Comprehensive distance-preserving autoencoders for cross-modal retrieval
Yibing Zhan, Jun Yu, Zhou Yu, Rong Zhang, Dacheng Tao, and Qi Tian · 2018
Later among the works it cites.
Rate-distortion optimization guided autoencoder for isometric embedding in euclidean latent space
Keizo Kato, Jing Zhou, Tomotake Sasaki, and Akira Nakagawa · 2019
Later among the works it cites.
Dimal: Deep isometric manifold learning using sparse geodesic sampling
Gautam Pai, Ronen Talmon, Alex Bronstein, and Ron Kimmel · 2019
Later among the works it cites.
Variational laplace autoencoders
Yookoon Park, Chris Dongjoo Kim, and Gunhee Kim · 2019
Later among the works it cites.
From variational to deterministic autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf · 2020
Closest in time.
On implicit regularization in β \beta -vaes
Abhishek Kumar and Ben Poole · 2020
Closest in time.
Regularized autoencoders via relaxed injective probability flow
Abhishek Kumar, Ben Poole, and Kevin Murphy · 2020
Closest in time.
Loca: Local conformal autoencoder for standardized data coordinates
Erez Peterfreund, Ofir Lindenbaum, Felix Dietrich, Tom Bertalan, Matan Gavish, Ioannis G Kevrekidis, and Ronald R Coifman · 2020
Closest in time.