Fetching the paper…
Reading the bibliography…
Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.
A new approach to linear filtering and prediction problems
R. Kalman · 1960
Earlier work this paper cites.
A wavelet tour of signal processing
S. Mallat · 1999
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, et al · 2004
Earlier work this paper cites.
Human3.6
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu · 2013
Earlier work this paper cites.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
Earlier work this paper cites.
Action-conditional video prediction using deep networks in
J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. Singh · 2015
Earlier work this paper cites.
Spatio-temporal video autoencoder with differentiable memory
V. Patraucean, A. Handa, and R. Cipolla · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using
N. Srivastava, E. Mansimov, and R. Salakhudinov · 2015
Earlier work this paper cites.
Embed to control: A locally linear latent dynamics model for control from raw images
M. Watter, J. Springenberg, J. Boedecker, and M. Riedmiller · 2015
Earlier work this paper cites.
Convolutional
S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo · 2015
Earlier work this paper cites.
Data assimilation: methods, algorithms, and applications
M. Asch, M. Bocquet, and M. Nodet · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, et al · 2016
Earlier work this paper cites.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
S. L. Brunton, J. L. Proctor, and J. N. Kutz · 2016
Earlier work this paper cites.
Attend, infer, repeat: Fast scene understanding with generative models
S. A. Eslami, N. Heess, T. Weber, Y. Tassa, D. Szepesvari, G. E. Hinton, et al · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Earlier work this paper cites.
Backprop
T. Haarnoja, A. Ajay, S. Levine, and P. Abbeel · 2016
Earlier work this paper cites.
Dynamic filter networks
X. Jia, B. De Brabandere, T. Tuytelaars, and L. V. Gool · 2016
Earlier work this paper cites.
Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
Earlier work this paper cites.
Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
T. Xue, J. Wu, K. Bouman, and B. Freeman · 2016
Earlier work this paper cites.
Soft-dtw: a differentiable loss function for time-series
M. Cuturi and M. Blondel · 2017
Earlier work this paper cites.
Unsupervised learning of disentangled representations from video
E. L. Denton et al · 2017
Earlier work this paper cites.
Image restoration: Wavelet frame shrinkage, nonlinear evolution
B. Dong, Q. Jiang, and Z. Shen · 2017
Earlier work this paper cites.
A disentangled recognition and nonlinear dynamics model for unsupervised learning
M. Fraccaro, S. Kamronn, U. Paquet, and O. Winther · 2017
Cited alongside, same era.
Dual motion
X. Liang, L. Lee, W. Dai, and E. P. Xing · 2017
Cited alongside, same era.
Video frame synthesis using deep voxel flow
Z. Liu, R. A. Yeh, X. Tang, Y. Liu, and A. Agarwala · 2017
Cited alongside, same era.
Flexible spatio-temporal networks for video prediction
C. Lu, M. Hirsch, and B. Scholkopf · 2017
Cited alongside, same era.
Unsupervised learning of long-term motion dynamics for videos
Z. Luo, B. Peng, D.-A. Huang, A. Alahi, and L. Fei-Fei · 2017
Cited alongside, same era.
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2017
Flexible neural representation for physics prediction
D. Mrowca, C. Zhuang, E. Wang, N. Haber, L. F. Fei-Fei, J. Tenenbaum, and D. L. Yamins · 2018
Later among the works it cites.
Folded recurrent neural networks for future video prediction
M. Oliu, J. Selva, and S. Escalera · 2018
Later among the works it cites.
Recurrent relational networks
R. Palm, U. Paquet, and O. Winther · 2018
Later among the works it cites.
Deep hidden physics models: Deep learning of nonlinear partial differential equations
M. Raissi · 2018
Later among the works it cites.
Hybridnet: Classification and reconstruction cooperation for semi-supervised learning
T. Robert, N. Thome, and M. Cord · 2018
Later among the works it cites.
Graph networks as learnable physics engines for inference and control
A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. Riedmiller, R. Hadsell, and P. Battaglia · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Data-driven discovery of partial differential equations
S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz · 2017
Cited alongside, same era.
Learning partial differential equations via data discovery and sparse optimization
H. Schaeffer · 2017
Cited alongside, same era.
Decomposing motion and content for natural video sequence prediction
R. Villegas, J. Yang, S. Hong, X. Lin, and H. Lee · 2017
Cited alongside, same era.
Learning to generate long-term future via hierarchical prediction
R. Villegas, J. Yang, Y. Zou, S. Sohn, X. Lin, and H. Lee · 2017
Cited alongside, same era.
The pose knows: Video forecasting by generating pose futures
J. Walker, K. Marino, A. Gupta, and M. Hebert · 2017
Cited alongside, same era.
Visual interaction networks: Learning a physics simulator from video
N. Watters, D. Zoran, T. Weber, P. Battaglia, R. Pascanu, and A. Tacchetti · 2017
Cited alongside, same era.
Later among the works it cites.
Mocogan: Decomposing motion and content for video generation
S. Tulyakov, M.-Y. Liu, X. Yang, and J. Kautz · 2018
Later among the works it cites.
Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
S. van Steenkiste, M. Chang, K. Greff, and J. Schmidhuber · 2018
Later among the works it cites.
Structure preserving video prediction
J. Xu, B. Ni, Z. Li, S. Cheng, and X. Yang · 2018
Later among the works it cites.
Recurrent
P. Becker, H. Pandya, G. Gebhardt, C. Zhao, C. J. Taylor, and G. Neumann · 2019
Later among the works it cites.
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
M. Bocquet, J. Brajard, A. Carrassi, and L. Bertino · 2019
Later among the works it cites.
Improved conditional
L. Castrejon, N. Ballas, and A. Courville · 2019
Later among the works it cites.
Addressing failure prediction by learning model confidence
C. Corbière, N. Thome, A. Bar-Hen, M. Cord, and P. Pérez · 2019
Later among the works it cites.
Disentangling propagation and generation for video prediction
H. Gao, H. Xu, Q.-Z. Cai, R. Wang, F. Yu, and T. Darrell · 2019
Later among the works it cites.
Neural jump stochastic differential equations
J. Jia and A. R. Benson · 2019
Later among the works it cites.
Predicting future frames using retrospective cycle
Y.-H. Kwon and M.-G. Park · 2019
Later among the works it cites.
Shape and time distortion loss for training deep time series forecasting models
V. Le Guen and N. Thome · 2019
Later among the works it cites.
Unsupervised learning of object structure and dynamics from videos
M. Minderer, C. Sun, R. Villegas, F. Cole, K. Murphy, and H. Lee · 2019
Later among the works it cites.
Data driven governing equations approximation using deep neural networks
T. Qin, K. Wu, and D. Xiu · 2019
Later among the works it cites.
Differentiable physics-informed graph networks
S. Seo and Y. Liu · 2019
Later among the works it cites.
Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics
Y. Wang, J. Zhang, H. Zhu, M. Long, J. Wang, and P. S. Yu · 2019
Later among the works it cites.
Compositional video prediction
Y. Ye, M. Singh, A. Gupta, and S. Tulsiani · 2019
Later among the works it cites.
Convolutional neural networks combined with
M. Zhu, B. Chang, and C. Fu · 2019
Later among the works it cites.