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Parameter-exploring policy gradients
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in parameter space, whichExpand
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Learning Stochastic Recurrent Networks
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trainedExpand
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Policy Gradients with Parameter-Based Exploration for Control
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in parameter space, whichExpand
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On Fast Dropout and its Applicability to Recurrent Networks
Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modelling of RNNs, mostlyExpand
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Sequential Feature Selection for Classification
In most real-world information processing problems, data is not a free resource; its acquisition is rather time-consuming and/or expensive. We investigate how these two factors can be included inExpand
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NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
This paper introduces "Non-Autonomous Input-Output Stable Network" (NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamicalExpand
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Model-free robot anomaly detection
Safety is one of the key issues in the use of robots, especially when human-robot interaction is targeted. Although unforeseen environment situations, such as collisions or unexpected userExpand
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Image Super-Resolution with Fast Approximate Convolutional Sparse Coding
We present a computationally efficient architecture for image super-resolution that achieves state-of-the-art results on images with large spatial extend. Apart from utilizing Convolutional NeuralExpand
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Music Similarity Estimation with the Mean-Covariance Restricted Boltzmann Machine
Existing content-based music similarity estimation methods largely build on complex hand-crafted feature extractors, which are difficult to engineer. As an alternative, unsupervised machine learningExpand
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Using tactile sensation for learning contact knowledge: Discriminate collision from physical interaction
Detecting and interpreting contacts is a crucial aspect of physical Human-Robot Interaction. In order to discriminate between intended and unintended contact types, we derive a set of linear andExpand
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