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Optimal Compression of Locally Differentially Private Mechanisms

Oct 29, 2021
Abhin Shah, Wei-Ning Chen, Johannes Balle, Peter Kairouz, Lucas Theis

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Algorithms for the Communication of Samples

Oct 26, 2021
Lucas Theis, Noureldin Yosri

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A coding theorem for the rate-distortion-perception function

Apr 28, 2021
Lucas Theis, Aaron B. Wagner

* ICLR 2021 Neural Compression Workshop 

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On the advantages of stochastic encoders

Feb 18, 2021
Lucas Theis, Eirikur Agustsson

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Universally Quantized Neural Compression

Jun 17, 2020
Eirikur Agustsson, Lucas Theis

* Authors contributed equally 

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Discriminative Topic Modeling with Logistic LDA

Sep 03, 2019
Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis

* Advances in Neural Information Processing Systems 32, 2019 

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Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction

Jul 15, 2019
Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszar, Steven Yoo, Wenzhe Shi

* Accepted at RecSys '19 

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HoloGAN: Unsupervised learning of 3D representations from natural images

Apr 02, 2019
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang

* 8 pages, 9 figures 

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Faster gaze prediction with dense networks and Fisher pruning

Jul 09, 2018
Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár

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Fast Face-swap Using Convolutional Neural Networks

Jul 27, 2017
Iryna Korshunova, Wenzhe Shi, Joni Dambre, Lucas Theis

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

Jul 10, 2017
Andrew Aitken, Christian Ledig, Lucas Theis, Jose Caballero, Zehan Wang, Wenzhe Shi

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

May 25, 2017
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi

* 19 pages, 15 figures, 2 tables, accepted for oral presentation at CVPR, main paper + some supplementary material 

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Lossy Image Compression with Compressive Autoencoders

Mar 01, 2017
Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár

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Amortised MAP Inference for Image Super-resolution

Feb 21, 2017
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár

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Is the deconvolution layer the same as a convolutional layer?

Sep 22, 2016
Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew Aitken, Christian Ledig, Zehan Wang

* This is a note to share some additional insights for our the CVPR paper 

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A note on the evaluation of generative models

Apr 24, 2016
Lucas Theis, Aäron van den Oord, Matthias Bethge

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Inference and Mixture Modeling with the Elliptical Gamma Distribution

Dec 20, 2015
Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge

* Computational Statistics & Data Analysis 2016, Vol. 101, 29-43 
* 23 pages, 11 figures 

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Generative Image Modeling Using Spatial LSTMs

Sep 18, 2015
Lucas Theis, Matthias Bethge

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A Generative Model of Natural Texture Surrogates

May 28, 2015
Niklas Ludtke, Debapriya Das, Lucas Theis, Matthias Bethge

* 34 pages, 9 figures 

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

May 28, 2015
Lucas Theis, Matthew D. Hoffman

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

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

Apr 09, 2015
Matthias Kümmerer, Lucas Theis, Matthias Bethge

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Supervised learning sets benchmark for robust spike detection from calcium imaging signals

Feb 28, 2015
Lucas Theis, Philipp Berens, Emmanouil Froudarakis, Jacob Reimer, Miroslav Román Rosón, Tom Baden, Thomas Euler, Andreas Tolias, Matthias Bethge

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Mixtures of conditional Gaussian scale mixtures applied to multiscale image representations

Sep 20, 2011
Lucas Theis, Reshad Hosseini, Matthias Bethge

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In All Likelihood, Deep Belief Is Not Enough

Nov 28, 2010
Lucas Theis, Sebastian Gerwinn, Fabian Sinz, Matthias Bethge

* Journal of Machine Learning Research 12, 3071-3096, 2011 

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