199 citations · 301 across the 13 of their papers we have counts for
13 papers
Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech
Ilya Sklyar, Anna Piunova, Christian Osendorfer
Streaming recognition and segmentation of multi-party conversations with overlapping speech is crucial for the next generation of voice assistant applications. In this work we addr…
No Representation without Transformation
Giorgio Giannone, Saeed Saremi, Jonathan Masci +1
We extend the framework of variational autoencoders to represent transformations explicitly in the latent space. In the family of hierarchical graphical models that emerges, the la…
Recurrent Neural Processes
Timon Willi, Jonathan Masci, Jürgen Schmidhuber +1
We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models. RNPs model the state space with Neural Processes. Giv…
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer
Jan Svoboda, Asha Anoosheh, Christian Osendorfer +1
This paper introduces a neural style transfer model to generate a stylized image conditioning on a set of examples describing the desired style. The proposed solution produces high…
Deep Iterative Surface Normal Estimation
Jan Eric Lenssen, Christian Osendorfer, Jonathan Masci
This paper presents an end-to-end differentiable algorithm for robust and detail-preserving surface normal estimation on unstructured point-clouds. We utilize graph neural networks…
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
Marco Ciccone, Marco Gallieri, Jonathan Masci +2
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-autono…
Improving approximate RPCA with a k-sparsity prior
Maximilian Karl, Christian Osendorfer
A process centric view of robust PCA (RPCA) allows its fast approximate implementation based on a special form o a deep neural network with weights shared across all layers. Howeve…
Learning Stochastic Recurrent Networks
Justin Bayer, Christian Osendorfer
Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model…
Variational inference of latent state sequences using Recurrent Networks
Justin Bayer, Christian Osendorfer
Recent advances in the estimation of deep directed graphical models and recurrent networks let us contribute to the removal of a blind spot in the area of probabilistc modelling of…
On Fast Dropout and its Applicability to Recurrent Networks
Justin Bayer, Christian Osendorfer, Daniela Korhammer +3
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 modell…
Convolutional Neural Networks learn compact local image descriptors
Christian Osendorfer, Justin Bayer, Patrick van der Smagt
A standard deep convolutional neural network paired with a suitable loss function learns compact local image descriptors that perform comparably to state-of-the art approaches.
Unsupervised Feature Learning for low-level Local Image Descriptors
Christian Osendorfer, Justin Bayer, Sebastian Urban +1
Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amo…
Learning Sequence Neighbourhood Metrics
Justin Bayer, Christian Osendorfer, Patrick van der Smagt
Recurrent neural networks (RNNs) in combination with a pooling operator and the neighbourhood components analysis (NCA) objective function are able to detect the characterizing dyn…