
L. Theis,
T. Salimans,
M. D. Hoffman, and
F. Mentzer
Lossy Compression with Gaussian Diffusion
arXiv:2206.08889,
2022
#compression
#diffusion
#channel simulation
URL
RIS
BibTex


Y. Yang,
S. Mandt, and
L. Theis
An Introduction to Neural Data Compression
preprint,
2022
#neural compression
URL
RIS
BibTex


A. Shah,
W.N. Chen,
J. Balle,
P. Kairouz, and
L. Theis
Optimal Compression of Locally Differentially Private Mechanisms
Artificial Intelligence and Statistics,
2022
#differential privacy
#compression
#channel simulation
Code
URL
RIS
BibTex


L. Theis and
N. Yosri
Algorithms for the Communication of Samples
Proceedings of the 39th International Conference on Machine Learning,
2022
#compression
#information theory
#channel simulation
Code
URL
PDF
RIS
BibTex


L. Theis and
J. Ho
Importance weighted compression
Neural Compression Workshop at ICLR,
2021
#compression
#deep learning
#bits back
URL
RIS
BibTex


L. Theis and
A. B. Wagner
A coding theorem for the ratedistortionperception function
Neural Compression Workshop at ICLR,
2021
#compression
#information theory
URL
RIS
BibTex


L. Theis and
E. Agustsson
On the advantages of stochastic encoders
Neural Compression Workshop at ICLR,
2021
#compression
#information theory
URL
RIS
BibTex


E. Agustsson and
L. Theis
Universally Quantized Neural Compression
Advances in Neural Information Processing Systems 33,
2020
#compression
#deep learning
#channel simulation
URL
PDF
Appendix
RIS
BibTex


I. Korshunova,
H. Xiong,
M. Fedoryszak, and
L. Theis
Discriminative Topic Modeling with Logistic LDA
Advances in Neural Information Processing Systems 33,
2019
#lda
#bayesian inference
#topic modeling
Code
URL
PDF
Appendix
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T. NguyenPhuoc,
C. Li,
L. Theis,
C. Richardt, and
Y.L. Yang
HoloGAN: Unsupervised learning of 3D representations from natural images
International Conference on Computer Vision,
2019
#generative modeling
#3d
#deep learning
Code
URL
PDF
Video
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K. Storrs,
S. V. Leuven,
S. Kojder,
L. Theis, and
F. Huszár
Adaptive PairedComparison Method for Subjective Video Quality Assessment on Mobile Devices
Picture Coding Symposium,
2018
#compression
#perceptual quality
#psychophysics
URL
Blog
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BibTex


L. Theis,
I. Korshunova,
A. Tejani, and
F. Huszár
Faster gaze prediction with dense networks and Fisher pruning
arXiv:1801.05787,
2018
#pruning
#fisher information
#saliency
URL
Blog
RIS
BibTex


I. Korshunova,
W. Shi,
J. Dambre, and
L. Theis
Fast Faceswap Using Convolutional Neural Networks
International Conference on Computer Vision,
2017
#faceswap
#cagenet
#deep learning
URL
RIS
BibTex


C. Ledig,
L. Theis,
F. Huszar,
J. Caballero,
A. Aitken,
A. Tejani,
J. Totz,
Z. Wang,
et al.
PhotoRealistic Single Image SuperResolution Using a Generative Adversarial Network
Computer Vision and Pattern Recognition,
2017
#superresolution
#deep learning
URL
RIS
BibTex


L. Theis,
W. Shi,
A. Cunningham, and
F. Huszár
Lossy Image Compression with Compressive Autoencoders
International Conference on Learning Representations,
2017
#compression
#deep learning
URL
PDF
Poster
Files
RIS
BibTex


C. Sønderby,
J. Caballero,
L. Theis,
W. Shi, and
F. Huszár
Amortised MAP Inference for Image Superresolution
International Conference on Learning Representations,
2017
#superresolution
#deep learning
URL
RIS
BibTex


L. Theis,
P. Berens,
E. Froudarakis,
J. Reimer,
M. RomanRoson,
T. Baden,
T. Euler,
A. S. Tolias,
et al.
Benchmarking spike rate inference in population calcium imaging
Neuron,
90(3),
471482,
2016
#twophoton imaging
#spiking neurons
Code
URL
DOI
PDF
Poster
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BibTex


L. Theis,
A. van den Oord, and
M. Bethge
A note on the evaluation of generative models
International Conference on Learning Representations,
2016
#generative modeling
Code
URL
PDF
Talk
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L. Theis and
M. Bethge
Generative Image Modeling Using Spatial LSTMs
Advances in Neural Information Processing Systems 28,
2015
#deep learning
#generative modeling
#natural image statistics
#lstm
#mcgsm
Code
URL
PDF
Supplemental
Poster
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BibTex


L. Theis and
M. D. Hoffman
A trustregion method for stochastic variational inference with applications to streaming data
Proceedings of the 32nd International Conference on Machine Learning,
2015
#lda
#streaming
#svi
#bayesian inference
#topic modeling
Code
URL
PDF
Supplemental
Poster
Talk
RIS
BibTex


M. Kümmerer,
L. Theis, and
M. Bethge
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
ICLR Workshop,
2015
#saliency
#deep learning
URL
PDF
RIS
BibTex


H. E. Gerhard,
L. Theis, and
M. Bethge
Modeling Natural Image Statistics
Biologicallyinspired Computer Vision—Fundamentals and Applications,
Wiley VCH,
2015
#natural image statistics
#mcgsm
#ica
#psychophysics
URL
ISBN
PDF
RIS
BibTex


S. Sra,
R. Hosseini,
L. Theis, and
M. Bethge
Data modeling with the elliptical gamma distribution
Artificial Intelligence and Statistics,
2015
#density estimation
#natural image statistics
#mixture modeling
URL
PDF
RIS
BibTex


A. M. Chagas,
L. Theis,
B. Sengupta,
M. Stüttgen,
M. Bethge, and
C. Schwarz
Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents
Frontiers in Neural Circuits,
7(190),
2013
#neuroscience
#spiking neurons
URL
RIS
BibTex


L. Theis,
A. M. Chagas,
D. Arnstein,
C. Schwarz, and
M. Bethge
Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification
PLoS Computational Biology,
9(11),
2013
#generalized linear model
#spiking neurons
#mixture models
#generative modeling
Code
URL
DOI
PDF
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L. Theis,
J. SohlDickstein, and
M. Bethge
Training sparse natural image models with a fast Gibbs sampler of an extended state space
Advances in Neural Information Processing Systems 25,
2012
#natural image statistics
#ica
#overcompleteness
#bayesian inference
Code
PDF
Supplemental
Poster
RIS
BibTex


L. Theis,
R. Hosseini, and
M. Bethge
Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations
PLoS ONE,
7(7),
2012
#natural image statistics
#random fields
#mcgsm
#mixture models
Code
URL
DOI
PDF
RIS
BibTex


L. Theis,
S. Gerwinn,
F. Sinz, and
M. Bethge
In All Likelihood, Deep Belief Is Not Enough
Journal of Machine Learning Research,
12,
2011
#natural image statistics
#deep belief networks
#boltzmann machines
#deep learning
Code
PDF
RIS
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