335 citations · 499 across the 11 of their papers we have counts for
11 papers
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich +2
Lossy image compression aims to represent images in as few bits as possible while maintaining fidelity to the original. Theoretical results indicate that optimizing distortion metr…
Latent Discretization for Continuous-time Sequence Compression
Ricky T. Q. Chen, Matthew Le, Matthew Muckley +2
Neural compression offers a domain-agnostic approach to creating codecs for lossy or lossless compression via deep generative models. For sequence compression, however, most deep s…
An optimal control perspective on diffusion-based generative modeling
Julius Berner, Lorenz Richter, Karen Ullrich
We establish a connection between stochastic optimal control and generative models based on stochastic differential equations (SDEs), such as recently developed diffusion probabili…
Compressing Multisets with Large Alphabets using Bits-Back Coding
Daniel Severo, James Townsend, Ashish Khisti +2
Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real…
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…
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…
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.…
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…
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…
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…
Soft Weight-Sharing for Neural Network Compression
Karen Ullrich, Edward Meeds, Max Welling
The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices. This however, conflicts with their computationally, memor…