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Variational auto-encoders (VAEs) have proven to be a well suited tool for performing dimensionality reduction by extracting latent variables lying in a potentially much smaller dimensional space than the data.
A note on two problems in connexion with graphs
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The mnist database of handwritten digits
Yann LeCun · 1998
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Annealed importance sampling
Radford M Neal · 2001
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Simulating hamiltonian dynamics , volume 14
Benedict Leimkuhler and Sebastian Reich · 2004
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Hamiltonian importance sampling
Radford M Neal · 2005
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Geometric numerical integration: structure-preserving algorithms for ordinary differential equations , volume 31
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Open access series of imaging studies (oasis): Cross-sectional mri data in young, middle aged, nondemented, and demented older adults
Daniel S. Marcus, Tracy H. Wang, Jamie Parker, John G. Csernansky, John C. Morris, and Randy L. Buckner · 2007
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Monte Carlo strategies in scientific computing
Jun S Liu · 2008
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Riemannian manifold hamiltonian monte carlo
Mark Girolami, Ben Calderhead, and Siu A Chin · 2009
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Geodesic Methods in Computer Vision and Graphics
Gabriel Peyré, Mickael Péchaud, and Renaud Keriven · 2010
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Riemann manifold langevin and hamiltonian monte carlo methods
Mark Girolami and Ben Calderhead · 2011
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Scikit-learn: Machine learning in Python
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Radford M Neal · 2012
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Power failure: why small sample size undermines the reliability of neuroscience
Katherine S Button, John PA Ioannidis, Claire Mokrysz, Brian A Nosek, Jonathan Flint, Emma SJ Robinson, and Marcus R Munafò · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Hamiltonian variational auto-encoder
Anthony L Caterini, Arnaud Doucet, and Dino Sejdinovic · 2018
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Metrics for deep generative models
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, and Patrick Smagt · 2018
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Inference suboptimality in variational autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
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Hyperspherical variational auto-encoders
Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak · 2018
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Only bayes should learn a manifold (on the estimation of differential geometric structure from data)
Søren Hauberg · 2018
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Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Markov chain monte carlo and variational inference: Bridging the gap
Tim Salimans, Diederik Kingma, and Max Welling · 2015
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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 · 2016
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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Latent space oddity: on the curvature of deep generative models
Georgios Arvanitidis, Lars Kai Hansen, and Søren Hauberg · 2017
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The geometric foundations of hamiltonian monte carlo
Michael Betancourt, Simon Byrne, Sam Livingstone, Mark Girolami, et al · 2017
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On the convergence of hamiltonian monte carlo
Alain Durmus, Eric Moulines, and Eero Saksman · 2017
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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
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The riemannian geometry of deep generative models
Hang Shao, Abhishek Kumar, and P Thomas Fletcher · 2018
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Small sample sizes reduce the replicability of task-based fmri studies
Benjamin O Turner, Erick J Paul, Michael B Miller, and Aron K Barbey · 2018
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Geodesic clustering in deep generative models
Tao Yang, Georgios Arvanitidis, Dongmei Fu, Xiaogang Li, and Søren Hauberg · 2018
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Advances in variational inference
Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2018
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On the geometric ergodicity of hamiltonian monte carlo
Samuel Livingstone, Michael Betancourt, Simon Byrne, Mark Girolami, et al · 2019
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The continuous bernoulli: fixing a pervasive error in variational autoencoders
Gabriel Loaiza-Ganem and John P Cunningham · 2019
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Computational and statistical methods for trajectory analysis in a Riemannian geometry setting
Maxime Louis · 2019
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Diffusion variational autoencoders
Luis A Pérez Rey, Vlado Menkovski, and Jacobus W Portegies · 2019
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A contrastive divergence for combining variational inference and mcmc
Francisco JR Ruiz and Michalis K Titsias · 2019
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Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals
Denes Szucs and John PA Ioannidis · 2020
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