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Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
J. Lafferty, A. McCallum, and F. C. Pereira · 2001
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Discriminative training of kalman filters
P. Abbeel, A. Coates, M. Montemerlo, A. Y. Ng, and S. Thrun · 2005
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Discriminative density propagation for 3d human motion estimation
C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas · 2005
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Probabilistic Robotics
S. Thrun, W. Burgard, and D. Fox · 2005
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Combining discriminative features to infer complex trajectories
D. A. Ross, S. Osindero, and R. S. Zemel · 2006
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Neural network based state estimation of dynamical systems
N. Yadaiah and G. Sowmya · 2006
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A neural network implementing optimal state estimation based on dynamic spike train decoding
O. Bobrowski, R. Meir, S. Shoham, and Y. C. Eldar · 2007
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Conditional state space models for discriminative motion estimation
M. Kim and V. Pavlovic · 2007
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CRF-filters: Discriminative particle filters for sequential state estimation
B. Limketkai, D. Fox, and L. Liao · 2007
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Latent-dynamic discriminative models for continuous gesture recognition
L.-P. Morency, A. Quattoni, and T. Darrell · 2007
Cited alongside, same era.
Discriminatively trained particle filters for complex multi-object tracking
R. Hess and A. Fern · 2009
Cited alongside, same era.
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
J. Ko and D. Fox · 2009
Cited alongside, same era.
A neural implementation of the kalman filter
R. Wilson and L. Finkel · 2009
Cited alongside, same era.
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
G. E. Dahl, D. Yu, L. Deng, and A. Acero · 2012
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Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Neural conditional random fields
T. Do, T. Arti, et al · 2010
Cited alongside, same era.
Sequential deep learning for human action recognition
M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt · 2011
Cited alongside, same era.
R. G. Krishnan, U. Shalit, and D. Sontag · 2015
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J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
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Deep tracking: Seeing beyond seeing using recurrent neural networks
P. Ondruska and I. Posner · 2016
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