Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks

R. J. Williams and J. Peng, “An efficient gradient-based algorithm for on-line training of recurrent network trajectories,” Neural Computation , vol. 2, pp. 490–501, 1990

1990

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.

F. A. Gers, J. Schmidhuber, and F. A. Cummins, “Learning to forget: Continual prediction with lstm,” Neural computation , vol. 12, no. 10, pp. 2451–71, 10 2000

2000

Earlier work this paper cites.

F. A. Gers and J. Schmidhuber, “Recurrent nets that time and count,” in Proc. Intl. Joint Conf. Neural Networks , vol. 3, 2000, pp. 189–194

2000

Earlier work this paper cites.

A. P. Dempster, A Generalization of Bayesian Inference . Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 73–104

2008

Earlier work this paper cites.

R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in International Conference on Machine Learning (ICML) , 2013, pp. 1310–1318

2013

Earlier work this paper cites.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in Neural Information Processing Systems (NIPS) 27 . Curran Associates, Inc., 2014, pp. 3104–3112

2014

Earlier work this paper cites.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” ArXiv e-prints , Sept. 2014. [Online]. Available: http://arxiv.org/abs/1409.1556

Original

2014

Earlier work this paper cites.

W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent Neural Network Regularization,” ArXiv e-prints , Sept. 2014. [Online]. Available: http://arxiv.org/abs/1409.2329

Original

2014

Cited alongside, same era.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” ArXiv e-prints , Dec. 2014. [Online]. Available: http://arxiv.org/abs/1412.6980

Original

2014

Cited alongside, same era.

X. SHI, et al. , “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” in Advances in Neural Information Processing Systems (NIPS) 28 . Curran Associates, Inc., 2015, pp. 802–810

2015

Cited alongside, same era.

N. Srivastava, E. Mansimov, and R. Salakhutdinov, “Unsupervised learning of video representations using LSTMs,” in International Conference on Machine Learning (ICML) , 2015

2015

Cited alongside, same era.

P. Ondrúška and I. Posner, “Deep tracking: Seeing beyond seeing using recurrent neural networks,” in AAAI Conference on Artificial Intelligence , 2016, pp. 3361–3367

2016

Later among the works it cites.

P. Ondrúška, et al. , “End-to-end tracking and semantic segmentation using recurrent neural networks,” in Robotics: Science and Systems, Workshops , 2016

2016

Later among the works it cites.

D. Nuss, et al. , “A random finite set approach for dynamic occupancy grid maps with real-time application,” The International Journal of Robotics Research , vol. 37, no. 8, pp. 841–866, 2018

2018

Closest in time.

S. Hoermann, M. Bach, and K. Dietmayer, “Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling,” in ICRA , 2018

2018

Closest in time.