Fetching the paper…
Reading the bibliography…
Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience.
Aspects of the Theory of Syntax
Noam Chomsky · 1965
Earlier work this paper cites.
Independent component analysis, a new concept?
Pierre Comon · 1994
Earlier work this paper cites.
An information-maximization approach to blind separation and blind deconvolution
Anthony J Bell and Terrence J Sejnowski · 1995
Earlier work this paper cites.
Complexity of k-sat
R. Impagliazzo and R. Paturi · 1999
Earlier work this paper cites.
Weakly-supervised disentanglement without compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2002
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Attend, infer, repeat: Fast scene understanding with generative models
SM Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, Koray Kavukcuoglu, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Aapo Hyvärinen and Hiroshi Morioka · 2016
Earlier work this paper cites.
Deep directed generative models with energy-based probability estimation
Taesup Kim and Yoshua Bengio · 2016
Earlier work this paper cites.
A theory of generative convnet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu · 2016
Earlier work this paper cites.
Neural expectation maximization
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2017
Earlier work this paper cites.
Beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher P Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Earlier work this paper cites.
Nonlinear ica of temporally dependent stationary sources
Aapo Hyvärinen and Hiroshi Morioka · 2017
Earlier work this paper cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Cited alongside, same era.
Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
Cited alongside, same era.
Understanding disentangling in beta-vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
Cited alongside, same era.
Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
Later among the works it cites.
Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufmann, Rishab Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
Later among the works it cites.
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu · 2019
Later among the works it cites.
Variational autoencoders pursue pca directions (by accident)
Michal Rolinek, Dominik Zietlow, and Georg Martius · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Scan: Learning hierarchical compositional visual concepts
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, and Alexander Lerchner · 2018
Cited alongside, same era.
Nonlinear ica using auxiliary variables and generalized contrastive learning
Aapo Hyvärinen, Hiroaki Sasaki, and Richard E Turner · 2018
Cited alongside, same era.
Sequential attend, infer, repeat: Generative modelling of moving objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
Cited alongside, same era.
An intriguing failing of convolutional neural networks and the coordconv solution, 2018
Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski · 2018
Cited alongside, same era.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Cited alongside, same era.
Learning neural random fields with inclusive auxiliary generators
Yunfu Song and Zhijian Ou · 2018
Cited alongside, same era.
Generative models of visually grounded imagination
Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, and Kevin Murphy · 2018
Cited alongside, same era.
Aleksandar Stanić and Jürgen Schmidhuber · 2019
Later among the works it cites.
Entity abstraction in visual model-based reinforcement learning
Rishi Veerapaneni, John D Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B Tenenbaum, and Sergey Levine · 2019
Later among the works it cites.
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Kelsey R. Allen, Kevin A. Smith, and Joshua B. Tenenbaum · 2020
Later among the works it cites.
Virtual kitti 2, 2020
Yohann Cabon, Naila Murray, and Martin Humenberger · 2020
Later among the works it cites.
Compositional visual generation with energy based models
Yilun Du, Shuang Li, and Igor Mordatch · 2020
Later among the works it cites.
Genesis: Generative scene inference and sampling with object-centric latent representations
Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
Later among the works it cites.
Flow contrastive estimation of energy-based models
Ruiqi Gao, Erik Nijkamp, Diederik P Kingma, Zhen Xu, Andrew M Dai, and Ying Nian Wu · 2020
Later among the works it cites.
Semi-supervised StyleGAN for disentanglement learning
Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit Patel, and Animashree Anandkumar · 2020
Later among the works it cites.
On linear identifiability of learned representations
Geoffrey Roeder, Luke Metz, and Diedrik P. Kingma · 2020
Later among the works it cites.
Towards nonlinear disentanglement in natural data with temporal sparse coding
David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dylan Paiton · 2021
Closest in time.
Contrastive learning inverts the data generating process
Roland S Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2021
Closest in time.