Lucas Theis - RankMe

58 papers A* 11A 3B 2C 1Misc 1Journal 33Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Efficient Bayesian Inference from Noisy Pairwise Comparisons. CoRR Till Aczel, Lucas Theis, Roger Wattenhofer
2024	A*	`conf`	C3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video. CVPR Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont
2024	B	`conf`	Gaussian Channel Simulation with Rotated Dithered Quantization. ISIT Szymon Kobus, Lucas Theis, Deniz Gündüz
2024	J	`jnl`	Gaussian Channel Simulation with Rotated Dithered Quantization. CoRR Szymon Kobus, Lucas Theis, Deniz Gündüz
2024	A*	`conf`	Position: What makes an image realistic? ICML Lucas Theis
2024	A*	`conf`	The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. ICLR Daniel Severo, Lucas Theis, Johannes Ballé
2024	Misc	`conf`	Wasserstein Distortion: Unifying Fidelity and Realism. CISS Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis
2024	J	`jnl`	What makes an image realistic? CoRR Lucas Theis
2023	B	`conf`	Adaptive Greedy Rejection Sampling. ISIT Gergely Flamich, Lucas Theis
2023	J	`jnl`	Adaptive Greedy Rejection Sampling. CoRR Gergely Flamich, Lucas Theis
2023	J	`jnl`	An Introduction to Neural Data Compression. Found. Trends Comput. Graph. Vis. Yibo Yang, Stephan Mandt, Lucas Theis
2023	J	`jnl`	C3: High-performance and low-complexity neural compression from a single image or video. CoRR Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont
2023	J	`jnl`	High-Fidelity Image Compression with Score-based Generative Models. CoRR Emiel Hoogeboom, Eirikur Agustsson, Fabian Mentzer, Luca Versari, George Toderici, Lucas Theis
2023	J	`jnl`	The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. CoRR Daniel Severo, Lucas Theis, Johannes Ballé
2023	J	`jnl`	Wasserstein Distortion: Unifying Fidelity and Realism. CoRR Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis
2022	A*	`conf`	Algorithms for the Communication of Samples. ICML Lucas Theis, Noureldin Y. Ahmed
2022	J	`jnl`	An Introduction to Neural Data Compression. CoRR Yibo Yang, Stephan Mandt, Lucas Theis
2022	J	`jnl`	Lossy Compression with Gaussian Diffusion. CoRR Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer
2022	A	`conf`	Optimal Compression of Locally Differentially Private Mechanisms. AISTATS Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis
2021	J	`jnl`	A coding theorem for the rate-distortion-perception function. CoRR Lucas Theis, Aaron B. Wagner
2021	J	`jnl`	Algorithms for the Communication of Samples. CoRR Lucas Theis, Noureldin Yosri
2021	J	`jnl`	On the advantages of stochastic encoders. CoRR Lucas Theis, Eirikur Agustsson
2021	J	`jnl`	Optimal Compression of Locally Differentially Private Mechanisms. CoRR Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis
2020	A*	`conf`	Universally Quantized Neural Compression. NeurIPS Eirikur Agustsson, Lucas Theis
2020	J	`jnl`	Universally Quantized Neural Compression. CoRR Eirikur Agustsson, Lucas Theis
2019	J	`jnl`	Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction. CoRR Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszar, Steven Yoo, Wenzhe Shi
2019	A	`conf`	Addressing delayed feedback for continuous training with neural networks in CTR prediction. RecSys Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszár, Steven Yoo, Wenzhe Shi
2019	A*	`conf`	Discriminative Topic Modeling with Logistic LDA. NeurIPS Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis
2019	J	`jnl`	Discriminative Topic Modeling with Logistic LDA. CoRR Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis
2019	A*	`conf`	HoloGAN: Unsupervised Learning of 3D Representations From Natural Images. ICCV Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang
2019	—	`conf`	HoloGAN: Unsupervised Learning of 3D Representations From Natural Images. ICCV Workshops Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang
2019	J	`jnl`	HoloGAN: Unsupervised learning of 3D representations from natural images. CoRR Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang
2018	C	`conf`	Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices. PCS Katherine Storrs, Sebastiaan Van Leuven, Steve Kojder, Lucas Theis, Ferenc Huszar
2018	J	`jnl`	Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 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
2018	J	`jnl`	Faster gaze prediction with dense networks and Fisher pruning. CoRR Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár
2017	—	`conf`	Amortised MAP Inference for Image Super-resolution. ICLR Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár
2017	J	`jnl`	Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize. CoRR Andrew P. Aitken, Christian Ledig, Lucas Theis, Jose Caballero, Zehan Wang, Wenzhe Shi
2017	A*	`conf`	Fast Face-Swap Using Convolutional Neural Networks. ICCV Iryna Korshunova, Wenzhe Shi, Joni Dambre, Lucas Theis
2017	—	`conf`	Lossy Image Compression with Compressive Autoencoders. ICLR (Poster) Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár
2017	J	`jnl`	Lossy Image Compression with Compressive Autoencoders. CoRR Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár
2017	A*	`conf`	Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CVPR Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
2016	A*	`conf`	A note on the evaluation of generative models. ICLR Lucas Theis, Aäron van den Oord, Matthias Bethge
2016	J	`jnl`	Amortised MAP Inference for Image Super-resolution. CoRR Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár
2016	J	`jnl`	Fast Face-swap Using Convolutional Neural Networks. CoRR Iryna Korshunova, Wenzhe Shi, Joni Dambre, Lucas Theis
2016	J	`jnl`	Inference and mixture modeling with the Elliptical Gamma Distribution. Comput. Stat. Data Anal. Reshad Hosseini, Suvrit Sra, Lucas Theis, Matthias Bethge
2016	J	`jnl`	Is the deconvolution layer the same as a convolutional layer? CoRR Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew P. Aitken, Christian Ledig, Zehan Wang
2016	J	`jnl`	Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CoRR Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
2015	J	`jnl`	A Generative Model of Natural Texture Surrogates. CoRR Niklas Ludtke, Debapriya Das, Lucas Theis, Matthias Bethge
2015	A*	`conf`	A trust-region method for stochastic variational inference with applications to streaming data. ICML Lucas Theis, Matthew D. Hoffman
2015	—	`—`	Advances in probabilistic modeling of natural images. Lucas Theis
2015	A	`conf`	Data modeling with the elliptical gamma distribution. AISTATS Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge
2015	—	`conf`	Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. ICLR (Workshop) Matthias Kümmerer, Lucas Theis, Matthias Bethge
2015	—	`conf`	Generative Image Modeling Using Spatial LSTMs. NIPS Lucas Theis, Matthias Bethge
2015	J	`jnl`	Generative Image Modeling Using Spatial LSTMs. CoRR Lucas Theis, Matthias Bethge
2013	J	`jnl`	Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Comput. Biol. Lucas Theis, André Maia Chagas, Daniel Arnstein, Cornelius Schwarz, Matthias Bethge
2012	—	`conf`	Training sparse natural image models with a fast Gibbs sampler of an extended state space. NIPS Lucas Theis, Jascha Sohl-Dickstein, Matthias Bethge
2011	J	`jnl`	In All Likelihood, Deep Belief Is Not Enough. J. Mach. Learn. Res. Lucas Theis, Sebastian Gerwinn, Fabian H. Sinz, Matthias Bethge
2010	J	`jnl`	In All Likelihood, Deep Belief Is Not Enough CoRR Lucas Theis, Sebastian Gerwinn, Fabian H. Sinz, Matthias Bethge