In All Likelihood, Deep Belief Is Not Enough. - Lucas Theis; Sebastian Gerwinn; Fabian H. Sinz; Matthias Bethge (J. Mach. Learn. Res., 2011)

Training sparse natural image models with a fast Gibbs sampler of an extended state space. - Lucas Theis; Jascha Sohl-Dickstein; Matthias Bethge (NIPS, 2012)

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)

Data modeling with the elliptical gamma distribution. - Suvrit Sra; Reshad Hosseini; Lucas Theis; Matthias Bethge (AISTATS, 2015)

A trust-region method for stochastic variational inference with applications to streaming data. - Lucas Theis; Matthew D. Hoffman (ICML, 2015)

Generative Image Modeling Using Spatial LSTMs. - Lucas Theis; Matthias Bethge (NIPS, 2015)

Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. - Matthias Kümmerer; Lucas Theis; Matthias Bethge (ICLR (Workshop), 2015)

A Generative Model of Natural Texture Surrogates. - Niklas Ludtke; Debapriya Das; Lucas Theis; Matthias Bethge (CoRR, 2015)

Advances in probabilistic modeling of natural images. - Lucas Theis (2015)

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)

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)

A note on the evaluation of generative models. - Lucas Theis; Aäron van den Oord; Matthias Bethge (ICLR, 2016)

Inference and mixture modeling with the Elliptical Gamma Distribution. - Reshad Hosseini; Suvrit Sra; Lucas Theis; Matthias Bethge (Comput. Stat. Data Anal., 2016)

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 (CVPR, 2017)

Fast Face-Swap Using Convolutional Neural Networks. - Iryna Korshunova; Wenzhe Shi; Joni Dambre; Lucas Theis (ICCV, 2017)

Amortised MAP Inference for Image Super-resolution. - Casper Kaae Sønderby; Jose Caballero; Lucas Theis; Wenzhe Shi; Ferenc Huszár (ICLR, 2017)

Lossy Image Compression with Compressive Autoencoders. - Lucas Theis; Wenzhe Shi; Andrew Cunningham; Ferenc Huszár (ICLR (Poster), 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)

Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices. - Katherine Storrs; Sebastiaan Van Leuven; Steve Kojder; Lucas Theis; Ferenc Huszar (PCS, 2018)

Faster gaze prediction with dense networks and Fisher pruning. - Lucas Theis; Iryna Korshunova; Alykhan Tejani; Ferenc Huszár (CoRR, 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; et al. (PLoS Comput. Biol., 2018)

HoloGAN: Unsupervised Learning of 3D Representations From Natural Images. - Thu Nguyen-Phuoc; Chuan Li; Lucas Theis; Christian Richardt; Yong-Liang Yang (ICCV Workshops, 2019)

Discriminative Topic Modeling with Logistic LDA. - Iryna Korshunova; Hanchen Xiong; Mateusz Fedoryszak; Lucas Theis (NeurIPS, 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 (RecSys, 2019)

Universally Quantized Neural Compression. - Eirikur Agustsson; Lucas Theis (NeurIPS, 2020)

On the advantages of stochastic encoders. - Lucas Theis; Eirikur Agustsson (CoRR, 2021)

A coding theorem for the rate-distortion-perception function. - Lucas Theis; Aaron B. Wagner (CoRR, 2021)

Algorithms for the Communication of Samples. - Lucas Theis; Noureldin Yosri (CoRR, 2021)

Optimal Compression of Locally Differentially Private Mechanisms. - Abhin Shah; Wei-Ning Chen; Johannes Ballé; Peter Kairouz; Lucas Theis (AISTATS, 2022)

Algorithms for the Communication of Samples. - Lucas Theis; Noureldin Y. Ahmed (ICML, 2022)

Lossy Compression with Gaussian Diffusion. - Lucas Theis; Tim Salimans; Matthew D. Hoffman; Fabian Mentzer (CoRR, 2022)

Adaptive Greedy Rejection Sampling. - Gergely Flamich; Lucas Theis (ISIT, 2023)

High-Fidelity Image Compression with Score-based Generative Models. - Emiel Hoogeboom; Eirikur Agustsson; Fabian Mentzer; Luca Versari; George Toderici; Lucas Theis (CoRR, 2023)

An Introduction to Neural Data Compression. - Yibo Yang; Stephan Mandt; Lucas Theis (Found. Trends Comput. Graph. Vis., 2023)

Wasserstein Distortion: Unifying Fidelity and Realism. - Yang Qiu; Aaron B. Wagner; Johannes Ballé; Lucas Theis (CISS, 2024)

C3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video. - Hyunjik Kim; Matthias Bauer 001; Lucas Theis; Jonathan Richard Schwarz; Emilien Dupont (CVPR, 2024)

The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. - Daniel Severo 001; Lucas Theis; Johannes Ballé (ICLR, 2024)

Position: What makes an image realistic? - Lucas Theis (ICML, 2024)

Gaussian Channel Simulation with Rotated Dithered Quantization. - Szymon Kobus; Lucas Theis; Deniz Gündüz (ISIT, 2024)

What makes an image realistic? - Lucas Theis (CoRR, 2024)