Nicolas Heess

According to our database1, Nicolas Heess authored at least 62 papers between 2009 and 2018.

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Bibliography

2018
Maximum a Posteriori Policy Optimisation.
CoRR, 2018

Mix&Match - Agent Curricula for Reinforcement Learning.
CoRR, 2018

Relational inductive biases, deep learning, and graph networks.
CoRR, 2018

Graph networks as learnable physics engines for inference and control.
CoRR, 2018

Distributed Distributional Deterministic Policy Gradients.
CoRR, 2018

Learning by Playing - Solving Sparse Reward Tasks from Scratch.
CoRR, 2018

Reinforcement and Imitation Learning for Diverse Visuomotor Skills.
CoRR, 2018

Graph Networks as Learnable Physics Engines for Inference and Control.
Proceedings of the 35th International Conference on Machine Learning, 2018

Learning by Playing Solving Sparse Reward Tasks from Scratch.
Proceedings of the 35th International Conference on Machine Learning, 2018

Mix & Match Agent Curricula for Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Imagination-Augmented Agents for Deep Reinforcement Learning.
CoRR, 2017

Robust Imitation of Diverse Behaviors.
CoRR, 2017

FeUdal Networks for Hierarchical Reinforcement Learning.
CoRR, 2017

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards.
CoRR, 2017

Distral: Robust Multitask Reinforcement Learning.
CoRR, 2017

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation.
CoRR, 2017

Learning model-based planning from scratch.
CoRR, 2017

Learning human behaviors from motion capture by adversarial imitation.
CoRR, 2017

Filtering Variational Objectives.
CoRR, 2017

Particle Value Functions.
CoRR, 2017

Emergence of Locomotion Behaviours in Rich Environments.
CoRR, 2017

Metacontrol for Adaptive Imagination-Based Optimization.
CoRR, 2017

Learning Hierarchical Information Flow with Recurrent Neural Modules.
CoRR, 2017

Distral: Robust multitask reinforcement learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Filtering Variational Objectives.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning Hierarchical Information Flow with Recurrent Neural Modules.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Robust Imitation of Diverse Behaviors.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

FeUdal Networks for Hierarchical Reinforcement Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

Sim-to-Real Robot Learning from Pixels with Progressive Nets.
Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017

2016
Sample Efficient Actor-Critic with Experience Replay.
CoRR, 2016

Sim-to-Real Robot Learning from Pixels with Progressive Nets.
CoRR, 2016

Unsupervised Learning of 3D Structure from Images.
CoRR, 2016

Learning and Transfer of Modulated Locomotor Controllers.
CoRR, 2016

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models.
CoRR, 2016

Unsupervised Learning of 3D Structure from Images.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Gradient Estimation Using Stochastic Computation Graphs.
CoRR, 2015

Continuous control with deep reinforcement learning.
CoRR, 2015

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
CoRR, 2015

Passing Expectation Propagation Messages with Kernel Methods.
CoRR, 2015

Learning Continuous Control Policies by Stochastic Value Gradients.
CoRR, 2015

Memory-based control with recurrent neural networks.
CoRR, 2015

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Gradient Estimation Using Stochastic Computation Graphs.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Learning Continuous Control Policies by Stochastic Value Gradients.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
The Shape Boltzmann Machine: A Strong Model of Object Shape.
International Journal of Computer Vision, 2014

Recurrent Models of Visual Attention.
CoRR, 2014

Recurrent Models of Visual Attention.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Bayes-Adaptive Simulation-based Search with Value Function Approximation.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Deterministic Policy Gradient Algorithms.
Proceedings of the 31th International Conference on Machine Learning, 2014

Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Learning to Pass Expectation Propagation Messages.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012
Learning generative models of mid-level structure in natural images.
PhD thesis, 2012

Searching for objects driven by context.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Actor-Critic Reinforcement Learning with Energy-Based Policies.
Proceedings of the Tenth European Workshop on Reinforcement Learning, 2012

The Shape Boltzmann Machine: A strong model of object shape.
Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2011
Learning a Generative Model of Images by Factoring Appearance and Shape.
Neural Computation, 2011

Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs
CoRR, 2011

Multimodal Nonlinear Filtering Using Gauss-Hermite Quadrature.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

2009
Learning Generative Texture Models with extended Fields-of-Experts.
Proceedings of the British Machine Vision Conference, 2009


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