
L. Theis
What makes an image realistic?
Proceedings of the 41st International Conference on Machine Learning,
2024
#perceptual quality
#realism
#compression
#generative modeling
#outlier detection
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H. Kim,
M. Bauer,
L. Theis,
J. R. Schwarz, and
E. Dupont
C3: Highperformance and lowcomplexity neural compression from a single image or video
Computer Vision and Pattern Recognition,
2024
#compression
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D. Severo,
L. Theis, and
J. Ballé
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
International Conference on Learning Representations,
2024
#perceptual quality
#bapps
#lpips
#lasi
#ssim
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E. Hoogeboom,
E. Agustsson,
F. Mentzer,
L. Versari,
G. Toderici, and
L. Theis
HighFidelity Image Compression with Scorebased Generative Models
arXiv:2305.18231,
2023
#compression
#diffusion
#rectified flow
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G. Flamich and
L. Theis
Adaptive Greedy Rejection Sampling
IEEE International Symposium on Information Theory,
2023
#channel simulation
#compression
#information theory
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Y. Yang,
S. Mandt, and
L. Theis
An Introduction to Neural Data Compression
Foundations and Trends in Computer Graphics and Vision,
15(2),
113200,
2023
#compression
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L. Theis,
T. Salimans,
M. D. Hoffman, and
F. Mentzer
Lossy Compression with Gaussian Diffusion
arXiv:2206.08889,
2022
#compression
#diffusion
#channel simulation
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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
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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
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L. Theis and
J. Ho
Importance weighted compression
Neural Compression Workshop at ICLR,
2021
#compression
#deep learning
#bits back
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L. Theis and
A. B. Wagner
A coding theorem for the ratedistortionperception function
Neural Compression Workshop at ICLR,
2021
#compression
#information theory
#perceptual quality
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L. Theis and
E. Agustsson
On the advantages of stochastic encoders
Neural Compression Workshop at ICLR,
2021
#compression
#information theory
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E. Agustsson and
L. Theis
Universally Quantized Neural Compression
Advances in Neural Information Processing Systems 33,
2020
#compression
#deep learning
#channel simulation
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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
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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
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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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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
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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
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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
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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
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Poster
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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
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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
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DOI
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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
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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
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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
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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
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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
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ISBN
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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
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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
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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
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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
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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
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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
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