Fetching the paper…
Reading the bibliography…
Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data.
Hierarchical grouping to optimize an objective function
Joe H Ward Jr · 1963
Earlier work this paper cites.
Estimation of a multivariate density
Theophilos Cacoullos · 1966
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Maximum likelihood estimation of intrinsic dimension
Elizaveta Levina and Peter Bickel · 2004
Earlier work this paper cites.
Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms
Stan Salvador and Philip Chan · 2004
Earlier work this paper cites.
Comments on’maximum likelihood estimation of intrinsic dimension’by e. levina and p. bickel (2004)
David JC MacKay and Zoubin Ghahramani · 2005
Earlier work this paper cites.
Clustering methods
Lior Rokach and Oded Maimon · 2005
Earlier work this paper cites.
k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
Earlier work this paper cites.
Measure theory , volume 2
Vladimir Igorevich Bogachev · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Submanifold density estimation
Arkadas Ozakin and Alexander Gray · 2009
Earlier work this paper cites.
Sample complexity of testing the manifold hypothesis
Hariharan Narayanan and Sanjoy Mitter · 2010
Earlier work this paper cites.
Sparse manifold clustering and embedding
Ehsan Elhamifar and René Vidal · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Subspace clustering
René Vidal · 2011
Earlier work this paper cites.
Sparse subspace clustering: Algorithm, theory, and applications, 2012
Ehsan Elhamifar and Rene Vidal · 2012
Earlier work this paper cites.
Revisiting k-means: new algorithms via bayesian nonparametrics
Brian Kulis and Michael I Jordan · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Sparse subspace clustering: Algorithm, theory, and applications
Ehsan Elhamifar and René Vidal · 2013
Earlier work this paper cites.
Introduction to smooth manifolds
John M Lee · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
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, et al · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
Earlier work this paper cites.
Normalizing flows on riemannian manifolds
Mevlana C Gemici, Danilo Rezende, and Shakir Mohamed · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Approximate inference for deep latent gaussian mixtures
Eric Nalisnick, Lars Hertel, and Padhraic Smyth · 2016
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Cited alongside, same era.
Generative mixture of networks
Ershad Banijamali, Ali Ghodsi, and Pascal Popuart · 2017
Cited alongside, same era.
Deep unsupervised clustering with gaussian mixture variational autoencoders
Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan · 2017
Ensembles of generative adversarial networks for disconnected data
Lorenzo Luzi, Randall Balestriero, and Richard G Baraniuk · 2020
Later among the works it cites.
Riemannian continuous normalizing flows
Emile Mathieu and Maximilian Nickel · 2020
Later among the works it cites.
Variational mixture of normalizing flows
Guilherme GP Freitas Pires and Mário AT Figueiredo · 2020
Later among the works it cites.
Invertible gaussian reparameterization: Revisiting the gumbel-softmax
Andres Potapczynski, Gabriel Loaiza-Ganem, and John P Cunningham · 2020
Later among the works it cites.
Normalizing flows on tori and spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, and Kyle Cranmer · 2020
Later among the works it cites.
Segma: Semi-supervised gaussian mixture autoencoder
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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
Cited alongside, same era.
Variational deep embedding: an unsupervised and generative approach to clustering
Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou · 2017
Cited alongside, same era.
Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Matan Ben-Yosef and Daphna Weinshall · 2018
Cited alongside, same era.
Disconnected manifold learning for generative adversarial networks
Mahyar Khayatkhoei, Maneesh K Singh, and Ahmed Elgammal · 2018
Cited alongside, same era.
Marek Śmieja, Maciej Wołczyk, Jacek Tabor, and Bernhard C Geiger · 2020
Later among the works it cites.
Learning disconnected manifolds: a no gan’s land
Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, and Jeremie Mary · 2020
Later among the works it cites.
Beyond linear subspace clustering: A comparative study of nonlinear manifold clustering algorithms
Maryam Abdolali and Nicolas Gillis · 2021
Later among the works it cites.
Generalized energy based models
Michael Arbel, Liang Zhou, and Arthur Gretton · 2021
Later among the works it cites.
Intrinsic dimension estimation
Adam Block, Zeyu Jia, Yury Polyanskiy, and Alexander Rakhlin · 2021
Later among the works it cites.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Later among the works it cites.
Rectangular flows for manifold learning
Anthony L Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, and John P Cunningham · 2021
Later among the works it cites.
Transport Monte Carlo: High-accuracy posterior approximation via random transport
Leo L Duan · 2021
Later among the works it cites.
Trumpets: Injective flows for inference and inverse problems
Konik Kothari, AmirEhsan Khorashadizadeh, Maarten de Hoop, and Ivan Dokmanić · 2021
Later among the works it cites.
Tangent space and dimension estimation with the wasserstein distance
Uzu Lim, Vidit Nanda, and Harald Oberhauser · 2021
Later among the works it cites.
The intrinsic dimension of images and its impact on learning
Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
Later among the works it cites.
Tractable density estimation on learned manifolds with conformal embedding flows
Brendan Leigh Ross and Jesse C Cresswell · 2021
Later among the works it cites.
Counterexamples in Measure and Integration
René L Schilling and Franziska Kühn · 2021
Later among the works it cites.
Lifelong mixture of variational autoencoders
Fei Ye and Adrian G Bors · 2021
Later among the works it cites.
Efficient deep embedded subspace clustering
Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, and Zhao Zhang · 2022
Closest in time.
Edmond Cunningham, Adam Cobb, and Susmit Jha · 2022
Closest in time.
Riemannian score-based generative modeling
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet · 2022
Closest in time.
Diagnosing and fixing manifold overfitting in deep generative models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C Cresswell, and Anthony L. Caterini · 2022
Closest in time.
Neural implicit manifold learning for topology-aware generative modelling
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L Caterini, and Jesse C Cresswell · 2022
Closest in time.
Can push-forward generative models fit multimodal distributions?
Antoine Salmona, Valentin de Bortoli, Julie Delon, and Agnès Desolneux · 2022
Closest in time.
Lidl: Local intrinsic dimension estimation using approximate likelihood
Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek, Przemysław Spurek, Jacek Tabor, and Adam Golinski · 2022
Closest in time.
Bayesian nonparametrics for offline skill discovery
Valentin Villecroze, Harry J Braviner, Panteha Naderian, Chris J Maddison, and Gabriel Loaiza-Ganem · 2022
Closest in time.