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Lucas Theis
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2020 – today
- 2024
- [c24]Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis:
Wasserstein Distortion: Unifying Fidelity and Realism. CISS 2024: 1-6 - [c23]Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont:
C3: High-Performance and Low-Complexity Neural Compression from a Single Image or Video. CVPR 2024: 9347-9358 - [c22]Daniel Severo, Lucas Theis, Johannes Ballé:
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. ICLR 2024 - [c21]Lucas Theis:
Position: What makes an image realistic? ICML 2024 - [c20]Szymon Kobus, Lucas Theis, Deniz Gündüz:
Gaussian Channel Simulation with Rotated Dithered Quantization. ISIT 2024: 1907-1912 - [i27]Lucas Theis:
What makes an image realistic? CoRR abs/2403.04493 (2024) - [i26]Szymon Kobus, Lucas Theis, Deniz Gündüz:
Gaussian Channel Simulation with Rotated Dithered Quantization. CoRR abs/2407.12970 (2024) - 2023
- [j5]Yibo Yang, Stephan Mandt, Lucas Theis:
An Introduction to Neural Data Compression. Found. Trends Comput. Graph. Vis. 15(2): 113-200 (2023) - [c19]Gergely Flamich
, Lucas Theis:
Adaptive Greedy Rejection Sampling. ISIT 2023: 454-459 - [i25]Gergely Flamich, Lucas Theis:
Adaptive Greedy Rejection Sampling. CoRR abs/2304.10407 (2023) - [i24]Emiel Hoogeboom, Eirikur Agustsson, Fabian Mentzer, Luca Versari, George Toderici, Lucas Theis:
High-Fidelity Image Compression with Score-based Generative Models. CoRR abs/2305.18231 (2023) - [i23]Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis:
Wasserstein Distortion: Unifying Fidelity and Realism. CoRR abs/2310.03629 (2023) - [i22]Daniel Severo, Lucas Theis, Johannes Ballé:
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. CoRR abs/2310.05986 (2023) - [i21]Hyunjik Kim, Matthias Bauer, Lucas Theis, Jonathan Richard Schwarz, Emilien Dupont:
C3: High-performance and low-complexity neural compression from a single image or video. CoRR abs/2312.02753 (2023) - 2022
- [c18]Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis:
Optimal Compression of Locally Differentially Private Mechanisms. AISTATS 2022: 7680-7723 - [c17]Lucas Theis, Noureldin Y. Ahmed:
Algorithms for the Communication of Samples. ICML 2022: 21308-21328 - [i20]Yibo Yang, Stephan Mandt, Lucas Theis:
An Introduction to Neural Data Compression. CoRR abs/2202.06533 (2022) - [i19]Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer:
Lossy Compression with Gaussian Diffusion. CoRR abs/2206.08889 (2022) - 2021
- [i18]Lucas Theis, Eirikur Agustsson:
On the advantages of stochastic encoders. CoRR abs/2102.09270 (2021) - [i17]Lucas Theis, Aaron B. Wagner:
A coding theorem for the rate-distortion-perception function. CoRR abs/2104.13662 (2021) - [i16]Lucas Theis, Noureldin Yosri:
Algorithms for the Communication of Samples. CoRR abs/2110.12805 (2021) - [i15]Abhin Shah, Wei-Ning Chen, Johannes Ballé, Peter Kairouz, Lucas Theis:
Optimal Compression of Locally Differentially Private Mechanisms. CoRR abs/2111.00092 (2021) - 2020
- [c16]Eirikur Agustsson, Lucas Theis:
Universally Quantized Neural Compression. NeurIPS 2020 - [i14]Eirikur Agustsson, Lucas Theis:
Universally Quantized Neural Compression. CoRR abs/2006.09952 (2020)
2010 – 2019
- 2019
- [c15]Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images. ICCV 2019: 7587-7596 - [c14]Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang:
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images. ICCV Workshops 2019: 2037-2040 - [c13]Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis:
Discriminative Topic Modeling with Logistic LDA. NeurIPS 2019: 6767-6777 - [c12]Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszár, Steven Yoo, Wenzhe Shi:
Addressing delayed feedback for continuous training with neural networks in CTR prediction. RecSys 2019: 187-195 - [i13]Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang:
HoloGAN: Unsupervised learning of 3D representations from natural images. CoRR abs/1904.01326 (2019) - [i12]Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszar, Steven Yoo, Wenzhe Shi:
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction. CoRR abs/1907.06558 (2019) - [i11]Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis:
Discriminative Topic Modeling with Logistic LDA. CoRR abs/1909.01436 (2019) - 2018
- [j4]Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 14(5) (2018) - [c11]Katherine Storrs
Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices. PCS 2018: 169-173 - [i10]Lucas Theis, Iryna Korshunova, Alykhan Tejani, Ferenc Huszár:
Faster gaze prediction with dense networks and Fisher pruning. CoRR abs/1801.05787 (2018) - 2017
- [c10]Christian Ledig
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CVPR 2017: 105-114 - [c9]Iryna Korshunova, Wenzhe Shi, Joni Dambre
Fast Face-Swap Using Convolutional Neural Networks. ICCV 2017: 3697-3705 - [c8]Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár:
Amortised MAP Inference for Image Super-resolution. ICLR 2017 - [c7]Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár:
Lossy Image Compression with Compressive Autoencoders. ICLR (Poster) 2017 - [i9]Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár:
Lossy Image Compression with Compressive Autoencoders. CoRR abs/1703.00395 (2017) - [i8]Andrew P. Aitken, Christian Ledig, Lucas Theis, Jose Caballero, Zehan Wang, Wenzhe Shi:
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize. CoRR abs/1707.02937 (2017) - 2016
- [j3]Reshad Hosseini
Inference and mixture modeling with the Elliptical Gamma Distribution. Comput. Stat. Data Anal. 101: 29-43 (2016) - [c6]Lucas Theis, Aäron van den Oord, Matthias Bethge:
A note on the evaluation of generative models. ICLR 2016 - [i7]Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi:
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CoRR abs/1609.04802 (2016) - [i6]Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew P. Aitken, Christian Ledig, Zehan Wang:
Is the deconvolution layer the same as a convolutional layer? CoRR abs/1609.07009 (2016) - [i5]Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár:
Amortised MAP Inference for Image Super-resolution. CoRR abs/1610.04490 (2016) - [i4]Iryna Korshunova, Wenzhe Shi, Joni Dambre, Lucas Theis:
Fast Face-swap Using Convolutional Neural Networks. CoRR abs/1611.09577 (2016) - 2015
- [b1]Lucas Theis:
Advances in probabilistic modeling of natural images. University of Tübingen, Germany, 2015 - [c5]Suvrit Sra, Reshad Hosseini, Lucas Theis, Matthias Bethge:
Data modeling with the elliptical gamma distribution. AISTATS 2015 - [c4]Lucas Theis, Matthew D. Hoffman:
A trust-region method for stochastic variational inference with applications to streaming data. ICML 2015: 2503-2511 - [c3]Lucas Theis, Matthias Bethge:
Generative Image Modeling Using Spatial LSTMs. NIPS 2015: 1927-1935 - [c2]Matthias Kümmerer, Lucas Theis, Matthias Bethge:
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. ICLR (Workshop) 2015 - [i3]Niklas Ludtke, Debapriya Das, Lucas Theis, Matthias Bethge:
A Generative Model of Natural Texture Surrogates. CoRR abs/1505.07672 (2015) - [i2]Lucas Theis, Matthias Bethge:
Generative Image Modeling Using Spatial LSTMs. CoRR abs/1506.03478 (2015) - 2013
- [j2]Lucas Theis, André Maia Chagas
Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Comput. Biol. 9(11) (2013) - 2012
- [c1]Lucas Theis, Jascha Sohl-Dickstein, Matthias Bethge:
Training sparse natural image models with a fast Gibbs sampler of an extended state space. NIPS 2012: 1133-1141 - 2011
- [j1]Lucas Theis, Sebastian Gerwinn, Fabian H. Sinz, Matthias Bethge:
In All Likelihood, Deep Belief Is Not Enough. J. Mach. Learn. Res. 12: 3071-3096 (2011) - 2010
- [i1]Lucas Theis, Sebastian Gerwinn, Fabian H. Sinz, Matthias Bethge:
In All Likelihood, Deep Belief Is Not Enough. CoRR abs/1011.6086 (2010)
Coauthor Index
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last updated on 2024-10-12 22:57 CEST by the dblp team
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