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In autonomous navigation, a planning system reasons about other agents to plan a safe and plausible trajectory.
T. Fraichard, “Dynamic trajectory planning with dynamic constraints: A ’state-time space’ approach,” in IROS , 1993
1993
Earlier work this paper cites.
D. J. Bemdt and J. Clifford, “Using dynamic time warping to find patterns in time series,” 1994
1994
Earlier work this paper cites.
F. Facchinei and A. Fischer, “On the accurate identification of active constraints,” SIAM Journal on Optimization , vol. 9, 08 1996
1996
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.
W. W. Cohen, R. E. Schapire, and Y. Singer, “Learning to order things,” in Advances in Neural Information Processing Systems 10 , M. I. Jordan, M. J. Kearns, and S. A. Solla, Eds. MIT Press, 1998, pp. 451–457
1998
Earlier work this paper cites.
Y. LeCun, P. Haffner, L. Bottou, and Y. Bengio, “Object recognition with gradient-based learning,” in Shape, Contour and Grouping in Computer Vision , 1999
1999
Earlier work this paper cites.
K. Järvelin and J. Kekäläinen, “IR evaluation methods for retrieving highly relevant documents,” in Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’00, 2000, pp. 41–48
2000
Earlier work this paper cites.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of statistics , pp. 1189–1232, 2001
2001
Earlier work this paper cites.
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, “Learning to rank using gradient descent,” in Proceedings of the 22nd International Conference on Machine Learning , ser. ICML ’05, 2005, pp. 89–96
2005
Earlier work this paper cites.
G. Ridgeway, “Generalized boosted models: A guide to the gbm package,” 2005
2005
Earlier work this paper cites.
C. J. Burges, R. Ragno, and Q. V. Le, “Learning to rank with nonsmooth cost functions,” in Advances in Neural Information Processing Systems 19 , B. Schölkopf, J. C. Platt, and T. Hoffman, Eds. MIT Press, 2007, pp. 193–200
2007
Earlier work this paper cites.
C. Urmson and W. R. Whittaker, “Self-driving cars and the urban challenge,” IEEE Intelligent Systems , vol. 23, no. 2, pp. 66–68, Mar. 2008
2008
Cited alongside, same era.
P. Li, Q. Wu, and C. J. Burges, “McRank: Learning to rank using multiple classification and gradient boosting,” in Advances in Neural Information Processing Systems 20 , J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, Eds. Curran Associates, Inc., 2008, pp. 897–904
2008
Cited alongside, same era.
H. Valizadegan, R. Jin, R. Zhang, and J. Mao, “Learning to rank by optimizing NDCG measure,” in Advances in Neural Information Processing Systems 22 , Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, Eds. Curran Associates, Inc., 2009, pp. 1883–1891
2009
Cited alongside, same era.
K. G. Jamieson and R. Nowak, “Active ranking using pairwise comparisons,” in Advances in Neural Information Processing Systems 24 , J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2011, pp. 2240–2248
R. Zhao, H. Ali, and P. van der Smagt, “Two-stream RNN/CNN for action recognition in 3d videos,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017
2017
Later among the works it cites.
X. Chu, W. Yang, W. Ouyang, C. Ma, A. L. Yuille, and X. Wang, “Multi-context attention for human pose estimation,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5669–5678, 2017
2017
Later among the works it cites.
E. Ohn-Bar and M. M. Trivedi, “Are all objects equal? Deep spatio-temporal importance prediction in driving videos,” Pattern Recogn. , vol. 64, no. C, pp. 425–436, Apr. 2017
2017
Later among the works it cites.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems , 2017, pp. 3146–3154
2017
Later among the works it cites.
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2011
Cited alongside, same era.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al. , “Scikit-learn: Machine learning in python,” Journal of machine learning research , vol. 12, no. Oct, pp. 2825–2830, 2011
2011
Cited alongside, same era.
Y. Wang, L. Wang, Y. Li, D. He, T.-Y. Liu, and W. Chen, “A theoretical analysis of NDCG type ranking measures,” in COLT , 2013
2013
Cited alongside, same era.
H. Yun, P. Raman, and S. Vishwanathan, “Ranking via robust binary classification,” in Advances in Neural Information Processing Systems 27 , Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 2582–2590
2014
Cited alongside, same era.
2016
Cited alongside, same era.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human trajectory prediction in crowded spaces,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
Cited alongside, same era.
D. Held, S. Thrun, and S. Savarese, “Learning to track at 100 fps with deep regression networks,” in European Conference on Computer Vision (ECCV) . Springer, October 2017
2017
Cited alongside, same era.
Y. Wu, J. Lim, and M.-H. Yang, “Object tracking benchmark,” IEEE Trans. Pattern Anal. Mach. Intell. , no. 9, pp. 1834–1848
Cited in the paper.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Cited in the paper.
N. Ponomareva, S. Radpour, G. Hendry, S. Haykal, T. Colthurst, P. Mitrichev, and A. Grushetsky, “TF boosted trees: A scalable tensorflow based framework for gradient boosting,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2017, pp. 423–427
2017
Later among the works it cites.
2017
Later among the works it cites.
A. Rudenko, L. Palmieri, and K. O. Arras, “Joint long-term prediction of human motion using a planning-based social force approach,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Brisbane , May 2018
2018
Later among the works it cites.
A. Tawari, P. Mallela, and S. Martin, “Learning to attend to salient targets in driving videos using fully convolutional RNN,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , Nov 2018, pp. 3225–3232
2018
Later among the works it cites.
S. Hecker, D. Dai, and L. V. Gool, “End-to-End learning of driving models with surround-view cameras and route planners,” in European Conference on Computer Vision (ECCV) , 2018
2018
Later among the works it cites.
M. Bansal, A. Krizhevsky, and A. Ogale, “ChauffeurNet: Learning to drive by imitating the best and synthesizing the worst,” in Proceedings of Robotics: Science and Systems , 2019
2019
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