Fetching the paper…
Reading the bibliography…
Spatiotemporal systems are common in the real-world.
R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering , vol. 82, no. 1, pp. 35–45, 1960
1960
Earlier work this paper cites.
D. R. Cox, “Prediction by exponentially weighted moving averages and related methods,” Journal of the Royal Statistical Society. Series B (Methodological) , pp. 414–422, 1961
1961
Earlier work this paper cites.
F. Rosenblatt, Perceptions and the theory of brain mechanisms . Spartan books, 1962
1962
Earlier work this paper cites.
A. Cliff and J. Ord, “Model building and the analysis of spatial pattern in human geography,” Journal of the Royal Statistical Society. Series B (Methodological) , vol. 37, no. 3, pp. 297–348, 1975
1975
Earlier work this paper cites.
A. Cliff and J. K. Ord, “Space-time modelling with an application to regional forecasting,” Transactions of the Institute of British Geographers , pp. 119–128, 1975
1975
Earlier work this paper cites.
A. G. Journel and C. J. Huijbregts, Mining geostatistics . Academic press, 1978
1978
Earlier work this paper cites.
P. E. Pfeifer and S. J. Deutsch, “A starima model-building procedure with application to description and regional forecasting,” Transactions of the Institute of British Geographers , pp. 330–349, 1980
1980
Earlier work this paper cites.
P. Smolensky, “Information processing in dynamical systems: Foundations of harmony theory,” DTIC Document, Tech. Rep., 1986
1986
Earlier work this paper cites.
J.-L. Lin and C. W. Granger, “Forecasting from non-linear models in practice,” Journal of Forecasting , vol. 13, no. 1, pp. 1–9, 1994
1994
Earlier work this paper cites.
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal, “The ”wake-sleep” algorithm for unsupervised neural networks,” Science , vol. 268, no. 5214, p. 1158, 1995
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
R. S. Sutton, D. A. McAllester, S. P. Singh, Y. Mansour et al. , “Policy gradient methods for reinforcement learning with function approximation.” in NIPS , 1999
1999
Earlier work this paper cites.
J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning . Springer series in statistics Springer, Berlin, 2001, vol. 1
2001
Earlier work this paper cites.
G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural Computation , vol. 14, no. 8, pp. 1771–1800, 2002
2002
Earlier work this paper cites.
M. Welling, M. Rosen-Zvi, and G. E. Hinton, “Exponential family harmoniums with an application to information retrieval,” in NIPS , 2004
2004
Earlier work this paper cites.
G. W. Taylor, G. E. Hinton, and S. T. Roweis, “Modeling human motion using binary latent variables,” in NIPS , 2006
2006
Earlier work this paper cites.
P. Del Moral, A. Doucet, and A. Jasra, “Sequential monte carlo samplers,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 68, no. 3, pp. 411–436, 2006
2006
Earlier work this paper cites.
J. G. De Gooijer and R. J. Hyndman, “25 years of time series forecasting,” International Journal of Forecasting , vol. 22, no. 3, pp. 443–473, 2006
2006
Earlier work this paper cites.
C. E. Rasmussen, “Gaussian processes for machine learning,” 2006
2006
Earlier work this paper cites.
J. Nocedal and S. Wright, Numerical optimization . Springer Science & Business Media, 2006
2006
Earlier work this paper cites.
B. R. Hunt, E. J. Kostelich, and I. Szunyogh, “Efficient data assimilation for spatiotemporal chaos: A local ensemble transform kalman filter,” Physica D: Nonlinear Phenomena , vol. 230, no. 1, pp. 112–126, 2007
2007
Earlier work this paper cites.
G. Chevillon, “Direct multi-step estimation and forecasting,” Journal of Economic Surveys , vol. 21, no. 4, pp. 746–785, 2007
2007
Earlier work this paper cites.
I. Sutskever and G. E. Hinton, “Learning multilevel distributed representations for high-dimensional sequences.” in AISTATS , 2007
2007
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in ICML , 2009
2009
Earlier work this paper cites.
I. Sutskever, G. E. Hinton, and G. W. Taylor, “The recurrent temporal restricted boltzmann machine,” in NIPS , 2009
2009
Earlier work this paper cites.
B. Recht, M. Fazel, and P. A. Parrilo, “Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,” SIAM Review , vol. 52, no. 3, pp. 471–501, 2010
2010
Earlier work this paper cites.
P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in IROS , 2010
2010
Earlier work this paper cites.
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in CVPR , 2010
2010
Earlier work this paper cites.
K. Yamaguchi, A. C. Berg, L. E. Ortiz, and T. L. Berg, “Who are you with and where are you going?” in CVPR , 2011
2011
Earlier work this paper cites.
A. Asahara, K. Maruyama, A. Sato, and K. Seto, “Pedestrian-movement prediction based on mixed markov-chain model,” in SIGSPATIAL , 2011
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks.” in AISTATS , 2011
2011
Earlier work this paper cites.
O. Ohashi and L. Torgo, “Wind speed forecasting using spatio-temporal indicators.” in ECAI , 2012
2012
Earlier work this paper cites.
K. P. Murphy, Machine learning: A probabilistic perspective . MIT press, 2012
2012
Earlier work this paper cites.
W. Mathew, R. Raposo, and B. Martins, “Predicting future locations with hidden markov models,” in UbiComp . ACM, 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in NIPS , 2012
2012
Cited alongside, same era.
M. Wytock and J. Z. Kolter, “Sparse gaussian conditional random fields: Algorithms, theory, and application to energy forecasting.” in ICML , 2013
2013
Cited alongside, same era.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in ICML , 2013
2013
Cited alongside, same era.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks.” in ICML , 2013
2013
Cited alongside, same era.
2014
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human trajectory prediction in crowded spaces,” in CVPR , 2016
2016
Later among the works it cites.
M. Mathieu, C. Couprie, and Y. LeCun, “Deep multi-scale video prediction beyond mean square error,” in ICLR , 2016
2016
Later among the works it cites.
C. Vondrick, H. Pirsiavash, and A. Torralba, “Generating videos with scene dynamics,” in NIPS , 2016
2016
Later among the works it cites.
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural-RNN: Deep learning on spatio-temporal graphs,” in CVPR , 2016
2016
Later among the works it cites.
R. Senanayake, S. O’Callaghan, and F. Ramos, “Predicting spatio-temporal propagation of seasonal influenza using variational gaussian process regression,” in AAAI , 2016
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
S. B. Taieb and R. Hyndman, “Boosting multi-step autoregressive forecasts,” in ICML , 2014
2014
Cited alongside, same era.
M. T. Bahadori, Q. R. Yu, and Y. Liu, “Fast multivariate spatio-temporal analysis via low rank tensor learning,” in NIPS , 2014
2014
Cited alongside, same era.
A. Mnih and K. Gregor, “Neural variational inference and learning in belief networks,” in ICML , 2014
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in ECCV , 2014
2014
Cited alongside, same era.
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and locally connected networks on graphs,” in ICLR , 2014
2014
Cited alongside, same era.
R. Mittelman, B. Kuipers, S. Savarese, and H. Lee, “Structured recurrent temporal restricted boltzmann machines,” in ICML , 2014
2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in NIPS , 2014
2014
Cited alongside, same era.
A. M. Lamb, A. G. ALIAS PARTH GOYAL, Y. Zhang, S. Zhang, A. C. Courville, and Y. Bengio, “Professor forcing: A new algorithm for training recurrent networks,” in NIPS , 2016
2016
Later among the works it cites.
I. Goodfellow, Y. Bengio, and A. Courville, “Deep learning,” 2016, book in preparation for MIT press. [Online]. Available: http://www.deeplearningbook.org
2016
Later among the works it cites.
C. Finn, I. Goodfellow, and S. Levine, “Unsupervised learning for physical interaction through video prediction,” in NIPS , 2016
2016
Later among the works it cites.
M. Fraccaro, S. r. K. Sø nderby, U. Paquet, and O. Winther, “Sequential neural models with stochastic layers,” in NIPS , 2016
2016
Later among the works it cites.
R. Yu and Y. Liu, “Learning from multiway data: Simple and efficient tensor regression,” in ICML , 2016
2016
Later among the works it cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in ICLR , 2016
2016
Later among the works it cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in NIPS , 2016
2016
Later among the works it cites.
X. Jia, B. De Brabandere, T. Tuytelaars, and L. V. Gool, “Dynamic filter networks,” in NIPS , 2016
2016
Later among the works it cites.
N. Kalchbrenner, A. v. d. Oord, K. Simonyan, I. Danihelka, O. Vinyals, A. Graves, and K. Kavukcuoglu, “Video pixel networks,” in ICML , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
C. Finn and S. Levine, “Deep visual foresight for planning robot motion,” in ICRA , 2017
2017
Later among the works it cites.
R. Yu, Y. Li, C. Shahabi, U. Demiryurek, and Y. Liu, “Deep learning: A generic approach for extreme condition traffic forecasting,” in SDM , 2017
2017
Later among the works it cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Later among the works it cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NIPS , 2017
2017
Later among the works it cites.
X. Shi, Z. Gao, L. Lausen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. WOO, “Deep learning for precipitation nowcasting: A benchmark and a new model,” in NIPS , 2017
2017
Later among the works it cites.
R. Villegas, J. Yang, S. Hong, X. Lin, and H. Lee, “Decomposing motion and content for natural video sequence prediction,” in ICLR , 2017
2017
Later among the works it cites.
Y. Wang, M. Long, J. Wang, Z. Gao, and P. S. Yu, “PredRNN: Recurrent neural networks for predictive learning using spatiotemporal lstms,” in NIPS , 2017
2017
Later among the works it cites.
A. Zonoozi, J. Kim, X. Li, and G. Cong, “Periodic-CRN: A convolutional recurrent model for crowd density prediction with recurring periodic patterns,” in IJCAI , 2018
2018
Closest in time.
Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in ICLR , 2018
2018
Closest in time.
J. Zhang, X. Shi, J. Xie, H. Ma, I. King, and D.-Y. Yeung, “GaAN: Gated attention networks for learning on large and spatiotemporal graphs,” in UAI , 2018
2018
Closest in time.
Y. Wang, Z. Gao, M. Long, J. Wang, and P. S. Yu, “PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning,” in ICML , 2018
2018
Closest in time.
Z. Xu, Y. Wang, M. Long, and J. Wang, “PredCNN: Predictive learning with cascade convolutions,” in IJCAI , 2018
2018
Closest in time.
A. Bhattacharyya, B. Schiele, and M. Fritz, “Accurate and diverse sampling of sequences based on a “best of many” sample objective,” in CVPR , 2018
2018
Closest in time.
Y. Jang, G. Kim, and Y. Song, “Video prediction with appearance and motion conditions,” in ICML , 2018
2018
Closest in time.
J. Xu, B. Ni, Z. Li, S. Cheng, and X. Yang, “Structure preserving video prediction,” in CVPR , 2018
2018
Closest in time.
B. Yu, H. Yin, and Z. Zhu, “Spatio–temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in IJCAI , 2018
2018
Closest in time.
I. Hasan, F. Setti, T. Tsesmelis, A. Del Bue, F. Galasso, and M. Cristani, “MX-LSTM: Mixing tracklets and vislets to jointly forecast trajectories and head poses,” in CVPR , 2018
2018
Closest in time.
M. Babaeizadeh, C. Finn, D. Erhan, R. H. Campbell, and S. Levine, “Stochastic variational video prediction,” in ICLR , 2018
2018
Closest in time.
E. Denton and R. Fergus, “Stochastic video generation with a learned prior,” in ICML , 2018
2018
Closest in time.