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No Representation without Transformation
We propose to extend Latent Variable Models with a simple idea: learn to...
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Recurrent Neural Processes
We extend Neural Processes (NPs) to sequential data through Recurrent NP...
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Conditional Neural Style Transfer with Peer-Regularized Feature Transform
This paper introduces a neural style transfer model to conditionally gen...
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Differentiable Iterative Surface Normal Estimation
This paper presents an end-to-end differentiable algorithm for anisotrop...
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NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
This paper introduces "Non-Autonomous Input-Output Stable Network" (NAIS...
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Improving approximate RPCA with a k-sparsity prior
A process centric view of robust PCA (RPCA) allows its fast approximate ...
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Learning Stochastic Recurrent Networks
Leveraging advances in variational inference, we propose to enhance recu...
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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...
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Convolutional Neural Networks learn compact local image descriptors
A standard deep convolutional neural network paired with a suitable loss...
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Unsupervised Feature Learning for low-level Local Image Descriptors
Unsupervised feature learning has shown impressive results for a wide ra...
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Learning Sequence Neighbourhood Metrics
Recurrent neural networks (RNNs) in combination with a pooling operator ...
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