activity
20172021
most citedOptical Music Recognition with Convolutional Sequence-to-Sequence Models

46 citations · 66 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

Yangjun Ruan, Karen Ullrich, Daniel Severo +5

Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal…

cs.LG2020

Neural Communication Systems with Bandwidth-limited Channel

Karen Ullrich, Fabio Viola, Danilo Jimenez Rezende

Reliably transmitting messages despite information loss due to a noisy channel is a core problem of information theory. One of the most important aspects of real world communicatio…

cs.LG20198 cited

Differentiable probabilistic models of scientific imaging with the Fourier slice theorem

Karen Ullrich, Rianne van den Berg, Marcus Brubaker +2

Scientific imaging techniques such as optical and electron microscopy and computed tomography (CT) scanning are used to study the 3D structure of an object through 2D observations.…

stat.ML201712 cited

Improved Bayesian Compression

Marco Federici, Karen Ullrich, Max Welling

Compression of Neural Networks (NN) has become a highly studied topic in recent years. The main reason for this is the demand for industrial scale usage of NNs such as deploying th…

cs.CV201746 cited

Optical Music Recognition with Convolutional Sequence-to-Sequence Models

Eelco van der Wel, Karen Ullrich

Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated…

stat.ML2017

Bayesian Compression for Deep Learning

Christos Louizos, Karen Ullrich, Max Welling

Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack t…