Lucas Theis

AI for Streaming Workshop 2024.

I recently left Google DeepMind to launch my own startup. At Google I worked on compression using neural networks and information theory. More broadly, I am interested in the application of probabilistic machine learning, information theory, and statistics to challenging real-world problems.

Before joining Google I worked at Twitter where I developed models for compression, super-resolution, topic modeling, image cropping, and other tasks. I joined Twitter through the acquisition of Magic Pony Technology, a London based video compression startup where I developed early transform coding approaches based on neural networks. And before that I did my PhD at the Max Planck Research School for Neural Information Processing in Tübingen, working in the lab of Matthias Bethge on some of the first deep generative models for images.

Resume lucas@theis.io

On the web

CrossValidated GitHub Twitter Facebook Flickr Pinboard

Publications

S. Kobus, L. Theis, and D. Gündüz
Gaussian Channel Simulation with Rotated Dithered Quantization
IEEE International Symposium on Information Theory, 2024
URL RIS BibTex
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
URL RIS BibTex
H. Kim, M. Bauer, L. Theis, J. R. Schwarz, and E. Dupont
C3: High-performance and low-complexity neural compression from a single image or video
Computer Vision and Pattern Recognition, 2024
#compression
Code URL Project RIS BibTex
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
URL RIS BibTex
E. Hoogeboom, E. Agustsson, F. Mentzer, L. Versari, G. Toderici, and L. Theis
High-Fidelity Image Compression with Score-based Generative Models
arXiv:2305.18231, 2023
#compression #diffusion #rectified flow
URL Files RIS BibTex
G. Flamich and L. Theis
Adaptive Greedy Rejection Sampling
IEEE International Symposium on Information Theory, 2023
#channel simulation #compression #information theory
URL PDF RIS BibTex
Y. Yang, S. Mandt, and L. Theis
An Introduction to Neural Data Compression
Foundations and Trends in Computer Graphics and Vision, 15(2), 113-200, 2023
#compression
URL PDF RIS BibTex
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
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 rate-distortion-perception function
Neural Compression Workshop at ICLR, 2021
#compression #information theory #perceptual quality
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 RIS BibTex
T. Nguyen-Phuoc, 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 RIS BibTex
K. Storrs, S. V. Leuven, S. Kojder, L. Theis, and F. Huszár
Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices
Picture Coding Symposium, 2018
#compression #perceptual quality #psychophysics
URL Blog RIS 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 Face-swap Using Convolutional Neural Networks
International Conference on Computer Vision, 2017
#face-swap #cagenet #deep learning
URL RIS BibTex
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Computer Vision and Pattern Recognition, 2017
#super-resolution #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 Super-resolution
International Conference on Learning Representations, 2017
#super-resolution #deep learning
URL RIS BibTex
L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, T. Euler, A. S. Tolias, et al.
Benchmarking spike rate inference in population calcium imaging
Neuron, 90(3), 471-482, 2016
#two-photon imaging #spiking neurons
Code URL DOI PDF Poster RIS 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 RIS BibTex
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 RIS BibTex
L. Theis and M. D. Hoffman
A trust-region 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
Biologically-inspired 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 RIS BibTex
L. Theis, J. Sohl-Dickstein, 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 BibTex