Karen Ullrich
Karen Ullrich
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Cited by
Cited by
Bayesian Compression for Deep Learning
C Louizos, K Ullrich, M Welling
Conference on Neural Information Processing Systems (NIPS) 2017, 2017
Soft Weight-Sharing for Neural Network Compression
K Ullrich, E Meeds, M Welling
International Conference on Learning Representations (ICLR), 2017
Boundary Detection in Music Structure Analysis using Convolutional Neural Networks.
K Ullrich, J Schlüter, T Grill
ISMIR, 417-422, 2014
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
E van der Wel, K Ullrich
International Society of Music Information Retrieval (ISMIR), 2017
Lossy Compression for Lossless Prediction
Y Dubois, B Bloem-Reddy, K Ullrich, CJ Maddison
Conference on Neural Information Processing Systems (NeurIPS) 2021, 2021
An optimal control perspective on diffusion-based generative modeling
J Berner, L Richter, K Ullrich
arXiv preprint arXiv:2211.01364, 2022
Methane storage and ebullition in monimolimnetic waters of polluted mine pit lake Vollert-Sued, Germany
C Horn, P Metzler, K Ullrich, M Koschorreck, B Boehrer
Science of the Total Environment 584, 1-10, 2017
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Y Ruan, K Ullrich, D Severo, J Townsend, A Khisti, A Doucet, A Makhzani, ...
International Conference on Machine Learning (ICML), 2021
Image compression with product quantized masked image modeling
A El-Nouby, MJ Muckley, K Ullrich, I Laptev, J Verbeek, H Jégou
arXiv preprint arXiv:2212.07372, 2022
Improved Bayesian Compression
M Federici, K Ullrich, M Welling
Bayesian Deep Learning Workshop at NIPS, 2017
Differentiable probabilistic models of scientific imaging with the Fourier slice theorem
K Ullrich, R Berg, M Brubaker, D Fleet, M Welling
Conference on Uncertainty in Artificial Intelligence (UAI), 2019
Structural segmentation with convolutional neural networks mirex submission
J Schlüter, K Ullrich, T Grill
Tenth running of the Music Information Retrieval Evaluation eXchange (MIREX …, 2014
Music transcription with convolutional sequence-to-sequence models
K Ullrich, E van der Wel
Improving statistical fidelity for neural image compression with implicit local likelihood models
MJ Muckley, A El-Nouby, K Ullrich, H Jégou, J Verbeek
International Conference on Machine Learning, 25426-25443, 2023
Compressing multisets with large alphabets
D Severo, J Townsend, A Khisti, A Makhzani, K Ullrich
IEEE Journal on Selected Areas in Information Theory 3 (4), 605-615, 2022
On the challenges and opportunities in generative ai
L Manduchi, K Pandey, R Bamler, R Cotterell, S Däubener, S Fellenz, ...
arXiv preprint arXiv:2403.00025, 2024
Your Dataset is a Multiset and You Should Compress it Like One
D Severo, J Townsend, AJ Khisti, A Makhzani, K Ullrich
NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021
An introduction to vision-language modeling
F Bordes, RY Pang, A Ajay, AC Li, A Bardes, S Petryk, O Mañas, Z Lin, ...
arXiv preprint arXiv:2405.17247, 2024
Neural communication systems with bandwidth-limited channel
K Ullrich, F Viola, DJ Rezende
arXiv preprint arXiv:2003.13367, 2020
Feed-forward neural networks for boundary detection in music structure analysis
K Ullrich
University of Amsterdam, 2014
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