Christian Osendorfer

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NNAISENSE ∙ Technische Universität München

No Representation without Transformation

We propose to extend Latent Variable Models with a simple idea: learn to...
12/09/2019 ∙ by Giorgio Giannone, et al. ∙ 13 ∙ share

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Recurrent Neural Processes

We extend Neural Processes (NPs) to sequential data through Recurrent NP...
06/13/2019 ∙ by Timon Willi, et al. ∙ 2 ∙ share

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Conditional Neural Style Transfer with Peer-Regularized Feature Transform

This paper introduces a neural style transfer model to conditionally gen...
06/07/2019 ∙ by Jan Svoboda, et al. ∙ 4 ∙ share

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Differentiable Iterative Surface Normal Estimation

This paper presents an end-to-end differentiable algorithm for anisotrop...
04/15/2019 ∙ by Jan Eric Lenssen, et al. ∙ 2 ∙ share

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NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

This paper introduces "Non-Autonomous Input-Output Stable Network" (NAIS...
04/19/2018 ∙ by Marco Ciccone, et al. ∙ 0 ∙ share

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Improving approximate RPCA with a k-sparsity prior

A process centric view of robust PCA (RPCA) allows its fast approximate ...
12/29/2014 ∙ by Maximilian Karl, et al. ∙ 0 ∙ share

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Learning Stochastic Recurrent Networks

Leveraging advances in variational inference, we propose to enhance recu...
11/27/2014 ∙ by Justin Bayer, et al. ∙ 0 ∙ share

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On Fast Dropout and its Applicability to Recurrent Networks

Recurrent Neural Networks (RNNs) are rich models for the processing of s...
11/04/2013 ∙ by Justin Bayer, et al. ∙ 0 ∙ share

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Convolutional Neural Networks learn compact local image descriptors

A standard deep convolutional neural network paired with a suitable loss...
04/30/2013 ∙ by Christian Osendorfer, et al. ∙ 0 ∙ share

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Unsupervised Feature Learning for low-level Local Image Descriptors

Unsupervised feature learning has shown impressive results for a wide ra...
01/14/2013 ∙ by Christian Osendorfer, et al. ∙ 0 ∙ share

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Learning Sequence Neighbourhood Metrics

Recurrent neural networks (RNNs) in combination with a pooling operator ...
09/09/2011 ∙ by Justin Bayer, et al. ∙ 0 ∙ share

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Christian Osendorfer

Featured Co-authors

Jürgen Schmidhuber

Patrick van der Smagt

Jonathan Masci

Justin Bayer

Faustino Gomez

Nutan Chen

Maximilian Karl

Sebastian Urban

Jan Eric Lenssen

Marco Gallieri

Marco Ciccone

No Representation without Transformation

Recurrent Neural Processes

Conditional Neural Style Transfer with Peer-Regularized Feature Transform

Differentiable Iterative Surface Normal Estimation

NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

Improving approximate RPCA with a k-sparsity prior

Learning Stochastic Recurrent Networks

On Fast Dropout and its Applicability to Recurrent Networks

Convolutional Neural Networks learn compact local image descriptors

Unsupervised Feature Learning for low-level Local Image Descriptors

Learning Sequence Neighbourhood Metrics