Lucas Theis

According to our database, Lucas Theis authored at least 38 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

What makes an image realistic?

Lucas Theis

CoRR, 2024

Wasserstein Distortion: Unifying Fidelity and Realism.

Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis

Proceedings of the 58th Annual Conference on Information Sciences and Systems, 2024

2023

An Introduction to Neural Data Compression.

Yibo Yang, Stephan Mandt, Lucas Theis

Found. Trends Comput. Graph. Vis., 2023

C3: High-performance and low-complexity neural compression from a single image or video.

Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont

CoRR, 2023

The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric.

Daniel Severo, Lucas Theis, Johannes Ballé

CoRR, 2023

High-Fidelity Image Compression with Score-based Generative Models.

Emiel Hoogeboom, Eirikur Agustsson, Fabian Mentzer, Luca Versari, George Toderici, Lucas Theis

CoRR, 2023

Adaptive Greedy Rejection Sampling.

Gergely Flamich, Lucas Theis

Proceedings of the IEEE International Symposium on Information Theory, 2023

2022

Lossy Compression with Gaussian Diffusion.

Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer

CoRR, 2022

Algorithms for the Communication of Samples.

Lucas Theis, Noureldin Y. Ahmed

Proceedings of the International Conference on Machine Learning, 2022

Optimal Compression of Locally Differentially Private Mechanisms.

Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Algorithms for the Communication of Samples.

Lucas Theis, Noureldin Yosri

CoRR, 2021

A coding theorem for the rate-distortion-perception function.

Lucas Theis, Aaron B. Wagner

CoRR, 2021

On the advantages of stochastic encoders.

Lucas Theis, Eirikur Agustsson

CoRR, 2021

2020

Universally Quantized Neural Compression.

Eirikur Agustsson, Lucas Theis

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Addressing delayed feedback for continuous training with neural networks in CTR prediction.

Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszár, Steven Yoo, Wenzhe Shi

Proceedings of the 13th ACM Conference on Recommender Systems, 2019

Discriminative Topic Modeling with Logistic LDA.

Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

HoloGAN: Unsupervised Learning of 3D Representations From Natural Images.

Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga, Patrick J. Mineault, Peter Rupprecht, Stephan Gerhard, Rainer W. Friedrich, Johannes Friedrich, Liam Paninski, Marius Pachitariu, Kenneth D. Harris, Ben Bolte, Timothy A. Machado, Dario Ringach, Jasmine Stone, Luke E. Rogerson, Nicolas J. Sofroniew, Jacob Reimer, Emmanouil Froudarakis, Thomas Euler, Miroslav Román Rosón, Lucas Theis, Andreas S. Tolias, Matthias Bethge

PLoS Comput. Biol., 2018

Faster gaze prediction with dense networks and Fisher pruning.

Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár

CoRR, 2018

Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices.

Katherine Storrs, Sebastiaan Van Leuven, Steve Kojder, Lucas Theis, Ferenc Huszar

Proceedings of the 2018 Picture Coding Symposium, 2018

2017

Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize.

Andrew P. Aitken, Christian Ledig, Lucas Theis, Jose Caballero, Zehan Wang, Wenzhe Shi

CoRR, 2017

Lossy Image Compression with Compressive Autoencoders.

Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár

Proceedings of the 5th International Conference on Learning Representations, 2017

Amortised MAP Inference for Image Super-resolution.

Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár

Proceedings of the 5th International Conference on Learning Representations, 2017

Fast Face-Swap Using Convolutional Neural Networks.

Iryna Korshunova, Wenzhe Shi, Joni Dambre, Lucas Theis

Proceedings of the IEEE International Conference on Computer Vision, 2017

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Inference and mixture modeling with the Elliptical Gamma Distribution.

Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge

Comput. Stat. Data Anal., 2016

A note on the evaluation of generative models.

Lucas Theis, Aäron van den Oord, Matthias Bethge

Proceedings of the 4th International Conference on Learning Representations, 2016

Is the deconvolution layer the same as a convolutional layer?

Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew P. Aitken, Christian Ledig, Zehan Wang

CoRR, 2016

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi

CoRR, 2016

2015

Advances in probabilistic modeling of natural images.

Lucas Theis

PhD thesis, 2015

A Generative Model of Natural Texture Surrogates.

Niklas Ludtke, Debapriya Das, Lucas Theis, Matthias Bethge

CoRR, 2015

Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet.

Matthias Kümmerer, Lucas Theis, Matthias Bethge

Proceedings of the 3rd International Conference on Learning Representations, 2015

Generative Image Modeling Using Spatial LSTMs.

Lucas Theis, Matthias Bethge

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A trust-region method for stochastic variational inference with applications to streaming data.

Lucas Theis, Matthew D. Hoffman

Proceedings of the 32nd International Conference on Machine Learning, 2015

Data modeling with the elliptical gamma distribution.

Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2013

Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification.

Lucas Theis, André Maia Chagas, Daniel Arnstein, Cornelius Schwarz, Matthias Bethge

PLoS Comput. Biol., 2013

2012

Training sparse natural image models with a fast Gibbs sampler of an extended state space.

Lucas Theis, Jascha Sohl-Dickstein, Matthias Bethge

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

In All Likelihood, Deep Belief Is Not Enough.

Lucas Theis, Sebastian Gerwinn, Fabian H. Sinz, Matthias Bethge

J. Mach. Learn. Res., 2011