Search Results for author:
Found 13 papers, 6 papers with code
Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech
Finally, we establish a novel framework for segmentation analysis of multi-party conversations through emission latency metrics.
No Representation without Transformation
To explicitly demonstrate the effect of these higher order objects, we show that the inferred latent transformations reflect interpretable properties in the observation space.
Recurrent Neural Processes
We extend Neural Processes (NPs) to sequential data through Recurrent NPs or RNPs, a family of conditional state space models.
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer
This paper introduces a neural style transfer model to generate a stylized image conditioning on a set of examples describing the desired style.
Deep Iterative Surface Normal Estimation
This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer.
Ranked #7 on
Surface Normals Estimation
on PCPNet
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 dynamical system.
Improving approximate RPCA with a k-sparsity prior
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.
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).
Variational inference of latent state sequences using Recurrent Networks
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 time series.
On Fast Dropout and its Applicability to Recurrent Networks
Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data.
Convolutional Neural Networks learn compact local image descriptors
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
Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision.
Learning Sequence Neighbourhood Metrics
Recurrent neural networks (RNNs) in combination with a pooling operator and the neighbourhood components analysis (NCA) objective function are able to detect the characterizing dynamics of sequences and embed them into a fixed-length vector space of arbitrary dimensionality.
