Herke van Hoof

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University of Amsterdam

Experimental design for MRI by greedy policy search

In today's clinical practice, magnetic resonance imaging (MRI) is routin...
10/30/2020 ∙ by Tim Bakker, et al. ∙ 9 ∙ share

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Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

Neural processes (NPs) constitute a family of variational approximate mo...
08/21/2020 ∙ by Qi Wang, et al. ∙ 15 ∙ share

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An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques

Air traffic control is becoming a more and more complex task due to the ...
07/03/2020 ∙ by Joris Mollinga, et al. ∙ 0 ∙ share

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MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

This paper introduces MDP homomorphic networks for deep reinforcement le...
06/30/2020 ∙ by Elise van der Pol, et al. ∙ 1 ∙ share

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Social navigation with human empowerment driven reinforcement learning

The next generation of mobile robots needs to be socially-compliant to b...
03/18/2020 ∙ by Tessa van der Heiden, et al. ∙ 5 ∙ share

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Estimating Gradients for Discrete Random Variables by Sampling without Replacement

We derive an unbiased estimator for expectations over discrete random va...
02/14/2020 ∙ by Wouter Kool, et al. ∙ 21 ∙ share

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Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Neural Network based controllers hold enormous potential to learn comple...
10/23/2019 ∙ by Sanjay Thakur, et al. ∙ 0 ∙ share

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Reinforcement Learning with Non-uniform State Representations for Adaptive Search

Efficient spatial exploration is a key aspect of search and rescue. In t...
06/15/2019 ∙ by Sandeep Manjanna, et al. ∙ 0 ∙ share

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Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

The well-known Gumbel-Max trick for sampling from a categorical distribu...
03/14/2019 ∙ by Wouter Kool, et al. ∙ 10 ∙ share

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Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Diversity of environments is a key challenge that causes learned robotic...
03/13/2019 ∙ by Sanjay Thakur, et al. ∙ 0 ∙ share

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Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challen...
12/04/2018 ∙ by Lucas Caccia, et al. ∙ 1 ∙ share

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BanditSum: Extractive Summarization as a Contextual Bandit

In this work, we propose a novel method for training neural networks to ...
09/25/2018 ∙ by Yue Dong, et al. ∙ 0 ∙ share

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Addressing Function Approximation Error in Actor-Critic Methods

In value-based reinforcement learning methods such as deep Q-learning, f...
02/26/2018 ∙ by Scott Fujimoto, et al. ∙ 0 ∙ share

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Policy Search with High-Dimensional Context Variables

Direct contextual policy search methods learn to improve policy paramete...
11/10/2016 ∙ by Voot Tangkaratt, et al. ∙ 0 ∙ share

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Herke van Hoof

Featured Co-authors

Masashi Sugiyama

Max Welling

Aaron Courville

Joelle Pineau

Doina Precup

Jan Peters

Qi Wang

Efstratios Gavves

Jackie Chi Kit Cheung

Frans A. Oliehoek

David Meger

Experimental design for MRI by greedy policy search

Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables

An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Social navigation with human empowerment driven reinforcement learning

Estimating Gradients for Discrete Random Variables by Sampling without Replacement

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

Reinforcement Learning with Non-uniform State Representations for Adaptive Search

Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

Deep Generative Modeling of LiDAR Data

BanditSum: Extractive Summarization as a Contextual Bandit

Addressing Function Approximation Error in Actor-Critic Methods

Policy Search with High-Dimensional Context Variables