Nicolas Heess
Nicolas Heess
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Cited by
Cited by
Continuous control with deep reinforcement learning
TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ...
arXiv preprint arXiv:1509.02971, 2015
Recurrent models of visual attention
V Mnih, N Heess, A Graves
Advances in neural information processing systems, 2204-2212, 2014
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
ICML, 2014
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
Emergence of locomotion behaviours in rich environments
N Heess, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ...
arXiv preprint arXiv:1707.02286, 2017
Sample efficient actor-critic with experience replay
Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ...
arXiv preprint arXiv:1611.01224, 2016
Feudal networks for hierarchical reinforcement learning
AS Vezhnevets, S Osindero, T Schaul, N Heess, M Jaderberg, D Silver, ...
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
Imagination-augmented agents for deep reinforcement learning
T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ...
arXiv preprint arXiv:1707.06203, 2017
Learning continuous control policies by stochastic value gradients
N Heess, G Wayne, D Silver, T Lillicrap, T Erez, Y Tassa
Advances in Neural Information Processing Systems, 2944-2952, 2015
Sim-to-real robot learning from pixels with progressive nets
AA Rusu, M Vecerik, T Rothörl, N Heess, R Pascanu, R Hadsell
arXiv preprint arXiv:1610.04286, 2016
Attend, infer, repeat: Fast scene understanding with generative models
SMA Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton
Advances in Neural Information Processing Systems, 3225-3233, 2016
Unsupervised learning of 3D structure from images
D Jimenez Rezende, SM Eslami, S Mohamed, P Battaglia, M Jaderberg, ...
arXiv preprint arXiv:1607.00662, 2016
Unsupervised learning of 3d structure from images
DJ Rezende, SMA Eslami, S Mohamed, P Battaglia, M Jaderberg, ...
Advances in Neural Information Processing Systems, 4996-5004, 2016
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M Večerík, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ...
arXiv preprint arXiv:1707.08817, 2017
Distral: Robust multitask reinforcement learning
Y Teh, V Bapst, WM Czarnecki, J Quan, J Kirkpatrick, R Hadsell, N Heess, ...
Advances in Neural Information Processing Systems, 4496-4506, 2017
Gradient estimation using stochastic computation graphs
J Schulman, N Heess, T Weber, P Abbeel
Advances in Neural Information Processing Systems, 3528-3536, 2015
Distributed distributional deterministic policy gradients
G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, A Muldal, ...
arXiv preprint arXiv:1804.08617, 2018
The shape boltzmann machine: a strong model of object shape
SMA Eslami, N Heess, CKI Williams, J Winn
International Journal of Computer Vision 107 (2), 155-176, 2014
The Shape Boltzmann Machine: A strong model of object shape
N Heess, S Eslami, J Winn
2012 IEEE Conference on Computer Vision and Pattern Recognition, 406-413, 2012
Graph networks as learnable physics engines for inference and control
A Sanchez-Gonzalez, N Heess, JT Springenberg, J Merel, M Riedmiller, ...
arXiv preprint arXiv:1806.01242, 2018
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