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An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world.
Challenge of spatial cognition for deep learning
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Weakly-supervised disentanglement without compromises
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Causality
Judea Pearl · 2009
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When training and test sets are different: characterizing learning transfer
Amos Storkey · 2009
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Transportability of causal and statistical relations: A formal approach
Judea Pearl and Elias Bareinboim · 2011
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Pure reasoning in 12-month-old infants as probabilistic inference
Ernő Téglás, Edward Vul, Vittorio Girotto, Michel Gonzalez, Joshua B Tenenbaum, and Luca L Bonatti · 2011
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Statistical learning theory: Models, concepts, and results
Ulrike Von Luxburg and Bernhard Schölkopf · 2011
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Controlling selection bias in causal inference
Elias Bareinboim and Judea Pearl · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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On causal and anticausal learning
B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. M. Mooij · 2012
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Simulation as an engine of physical scene understanding
Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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External validity: From do-calculus to transportability across populations
Judea Pearl, Elias Bareinboim, et al · 2014
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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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Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 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 learning in neural networks: An overview
Jürgen Schmidhuber · 2015
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Causal inference and the data-fusion problem
Elias Bareinboim and Judea Pearl · 2016
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Deep learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Aapo Hyvärinen and Hiroshi Morioka · 2016
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How image degradations affect deep cnn-based face recognition?
Samil Karahan, Merve Kilinc Yildirum, Kadir Kirtac, Ferhat Sukru Rende, Gultekin Butun, and Hazim Kemal Ekenel · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Renqiao Zhang, Jiajun Wu, Chengkai Zhang, William T. Freeman, and Joshua B. Tenenbaum · 2016
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Gauge equivariant convolutional networks and the icosahedral cnn
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, and Max Welling · 2019
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A comparative study of methods for measurement of energy of computing
Muhammad Fahad, Arsalan Shahid, Ravi Reddy Manumachu, and Alexey Lastovetsky · 2019
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Nonlinear ica of temporally dependent stationary sources
Aapo Hyvarinen and Hiroshi Morioka · 2017
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
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Ali Jahanian, Lucy Chai, and Phillip Isola · 2019
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Benchmarking robustness in object detection: Autonomous driving when winter is coming
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel · 2019
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Mnist-c: A robustness benchmark for computer vision
Norman Mu and Justin Gilmer · 2019
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Weakly supervised disentanglement with guarantees
Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2019
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Energy and policy considerations for deep learning in nlp, 2019
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
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Semi-generative modelling: Covariate-shift adaptation with cause and effect features
Julius von Kügelgen, Alexander Mey, and Marco Loog · 2019
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General E(2)-Equivariant Steerable CNNs
Maurice Weiler and Gabriele Cesa · 2019
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Deep set prediction networks
Yan Zhang, Jonathon Hare, and Adam Prugel-Bennett · 2019
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Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al · 2020
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How We Learn: Why Brains Learn Better Than Any Machine… for Now
Stanislas Dehaene · 2020
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On the transfer of disentangled representations in realistic settings
Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Inductive biases for deep learning of higher-level cognition
Anirudh Goyal and Yoshua Bengio · 2020
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On the binding problem in artificial neural networks
Klaus Greff, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2020
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Causal inference: what if
Miguel A Hernán and James M Robins · 2020
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Variational autoencoders and nonlinear ica: A unifying framework
Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen · 2020
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Towards nonlinear disentanglement in natural data with temporal sparse coding
David Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dylan Paiton · 2020
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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Assessing the suitability of the greenhouse gas protocol for calculation of emissions from public cloud computing workloads
David Mytton · 2020
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On linear identifiability of learned representations
Geoffrey Roeder, Luke Metz, and Diederik P Kingma · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien NP Martel, Alexander W Bergman, David B Lindell, and Gordon Wetzstein · 2020
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On disentangled representations learned from correlated data
Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer · 2020
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Do neural networks for segmentation understand insideness?
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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A theory of independent mechanisms for extrapolation in generative models
M. Besserve, R. Sun, D. Janzing, and B. Schölkopf · 2021
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