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Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc.
The condensation algorithm-conditional density propagation and applications to visual tracking
A. Blake and M. Isard · 1997
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Using the condensation algorithm for robust, vision-based mobile robot localization
F. Dellaert, W. Burgard, D. Fox, and S. Thrun · 1999
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An introduction to sequential monte carlo methods
A. Doucet, N. De Freitas, and N. Gordon · 2001
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Sequential monte carlo methods in practice. series statistics for engineering and information science, 2001
A. Doucet, N. De Freitas, and N. Gordon · 2001
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Robust monte carlo localization for mobile robots
S. Thrun, D. Fox, W. Burgard, and F. Dellaert · 2001
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Particle filters in robotics
S. Thrun · 2002
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Fastslam: A factored solution to the simultaneous localization and mapping problem
M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit, et al · 2002
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Learning probabilistic models of link structure
L. Getoor, N. Friedman, D. Koller, and B. Taskar · 2002
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Beyond the Kalman filter: Particle filters for tracking applications
B. Ristic, S. Arulampalam, and N. Gordon · 2003
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Model-based online learning of POMDPs
G. Shani, R. I. Brafman, and S. E. Shimony · 2005
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Probabilistic robotics
S. Thrun, W. Burgard, and D. Fox · 2005
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σ \sigma mcl: Monte-carlo localization for mobile robots with stereo vision
P. Elinas and J. J. Little · 2005
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Sequential monte carlo samplers
P. Del Moral, A. Doucet, and A. Jasra · 2006
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Supervised learning of topological maps using semantic information extracted from range data
O. Mozos and W. Burgard · 2006
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Monte Carlo strategies in scientific computing
J. S. Liu · 2008
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Ros: an open-source robot operating system
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng · 2009
Cited alongside, same era.
A tutorial on particle filtering and smoothing: Fifteen years later
A. Doucet and A. M. Johansen · 2009
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Closing the learning-planning loop with predictive state representations
B. Boots, S. M. Siddiqi, and G. J. Gordon · 2011
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Multi-observation sensor resetting localization with ambiguous landmarks
B. Coltin and M. Veloso · 2013
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Value prediction network
J. Oh, S. Singh, and H. Lee · 2017
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TreeQN and ATreeC: Differentiable tree planning for deep reinforcement learning
G. Farquhar, T. Rocktäschel, M. Igl, and S. Whiteson · 2017
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Optnet: Differentiable optimization as a layer in neural networks
B. Amos and J. Z. Kolter · 2017
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Path integral networks: End-to-end differentiable optimal control
M. Okada, L. Rigazio, and T. Aoshima · 2017
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Task-based end-to-end model learning in stochastic optimization
P. Donti, B. Amos, and J. Z. Kolter · 2017
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Cited alongside, same era.
Neural adaptive sequential monte carlo
S. Gu, Z. Ghahramani, and R. E. Turner · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
Cited alongside, same era.
Value iteration networks
A. Tamar, S. Levine, P. Abbeel, Y. Wu, and G. Thomas · 2016
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Reinforcement learning via recurrent convolutional neural networks
T. Shankar, S. K. Dwivedy, and P. Guha · 2016
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Backprop kf: Learning discriminative deterministic state estimators
T. Haarnoja, A. Ajay, S. Levine, and P. Abbeel · 2016
Cited alongside, same era.
End-to-end learnable histogram filters
R. Jonschkowski and O. Brock · 2016
Cited alongside, same era.
C. A. Naesseth, S. W. Linderman, R. Ranganath, and D. M. Blei · 2017
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Filtering variational objectives
C. J. Maddison, J. Lawson, G. Tucker, N. Heess, M. Norouzi, A. Mnih, A. Doucet, and Y. Teh · 2017
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Sedar-semantic detection and ranging: Humans can localise without lidar, can robots?
O. Mendez, S. Hadfield, N. Pugeault, and R. Bowden · 2017
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Semantic scene completion from a single depth image
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser · 2017
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Efficient processing of deep neural networks: A tutorial and survey
V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer · 2017
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Building generalizable agents with a realistic and rich 3d environment
Y. Wu, Y. Wu, G. Gkioxari, and Y. Tian · 2018
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Learning to search with MCTSnets
A. Guez, T. Weber, I. Antonoglou, K. Simonyan, O. Vinyals, D. Wierstra, R. Munos, and D. Silver · 2018
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Differentiable particle filters: End-to-end learning with algorithmic priors
R. Jonschkowski, D. Rastogi, and O. Brock · 2018
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Auto-encoding sequential monte carlo
T. A. Le, M. Igl, T. Rainforth, T. Jin, and F. Wood · 2018
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