Fetching the paper…
Reading the bibliography…
Visually predicting the stability of block towers is a popular task in the domain of intuitive physics.
Learning many related tasks at the same time with backpropagation
Rich Caruana · 1995
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1998
Earlier work this paper cites.
Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2015
Earlier work this paper cites.
Training deeper convolutional networks with deep supervision
Liwei Wang, Chen-Yu Lee, Zhuowen Tu, and Svetlana Lazebnik · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Earlier work this paper cites.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Cited alongside, same era.
Tutorial: GANs
Ian Goodfellow · 2016
Cited alongside, same era.
Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
Later among the works it cites.
Learning to navigate in complex environments
Piotr W. Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J. Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, and Raia Hadsell · 2017
Later among the works it cites.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Later among the works it cites.
Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, and Josh Tenenbaum · 2017
Later among the works it cites.
Addressing appearance change in outdoor robotics with adversarial domain adaptation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Autonomous robotic stone stacking with online next best object target pose planning
Fadri Furrer, Martin Wermelinger, Hironori Yoshida, Fabio Gramazio, Matthias Kohler, Roland Siegwart, and Marco Hutter · 2017
Cited alongside, same era.
Learning to pivot with adversarial networks
Gilles Louppe, Michael Kagan, and Kyle Cranmer · 2017
Cited alongside, same era.
UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
Ruihao Li, Sen Wang, Zhiqiang Long, and Dongbing Gu
Cited in the paper.
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
Wenbin Li, Jeannette Bohg, and Mario Fritz
Cited in the paper.
Markus Wulfmeier, Alex Bewley, and Ingmar Posner · 2017
Later among the works it cites.
Controllable invariance through adversarial feature learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard H. Hovy, and Graham Neubig · 2017
Later among the works it cites.
Shapestacks: Learning vision-based physical intuition for generalised object stacking
Oliver Groth, Fabian Fuchs, Ingmar Posner, and Andrea Vedaldi · 2018
Closest in time.