Lucas Theis

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Ferenc HuszárUniversity of CambridgeVerified email at cam.ac.uk
Zehan WangVerified email at zehanwang.com
Wenzhe Shi（施闻哲）ML@SharechatVerified email at sharechat.co
Matthias BethgeTübingen University & Maddox Co-FounderVerified email at bethgelab.org
Jose CaballeroShift LabVerified email at shiftlab.ai
Christian LedigProfessor of Explainable Machine Learning, University of BambergVerified email at uni-bamberg.de
Andrew AitkenTwitterVerified email at twitter.com
Alykhan TejaniSharechatVerified email at sharechat.co
Alejandro Acosta DiazTwitterVerified email at twitter.com
Iryna KorshunovaGhent UniversityVerified email at ugent.be
Andreas ToliasDepartment of Neuroscience, Baylor College of MedicineVerified email at cns.bcm.edu
Aäron van den OordGoogle DeepMindVerified email at google.com
Jacob ReimerAssistant Professor, Baylor College of MedicineVerified email at bcm.edu
Casper Kaae SønderbyFounder@twig.energy, ML/DL Research ScientistVerified email at twig.energy
Thomas EulerProfessor for Ophthalmic Research, CIN / Centre for Ophthalmology, University of TübingenVerified email at cin.uni-tuebingen.de
Matthias KümmererCentre for integrative Neuroscience, University TuebingenVerified email at bethgelab.org
Philipp BerensHertie Institute for AI in Brain Health, University of TübingenVerified email at uni-tuebingen.de
Tom BadenSussex Neuroscience, University of Sussex, UKVerified email at sussex.ac.uk
Reshad HosseiniAssistant Professor, Machine Learning and Robotics Group, University of TehranVerified email at tuebingen.mpg.de
Joni DambreHoofddocent, Ghent UniversityVerified email at ugent.be

Lucas Theis

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Machine Learning Compression Bayesian Inference Generative Modeling Deep Learning


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Photo-realistic single image super-resolution using a generative adversarial network C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ... Computer Vision and Pattern Recognition, 2017	10436	2017
A note on the evaluation of generative models L Theis, A van den Oord, M Bethge International Conference on Learning Representations, 2016	1087	2016
Lossy Image Compression with Compressive Autoencoders L Theis, W Shi, A Cunningham, F Huszár International Conference on Learning Representations, 2017	987	2017
Amortised MAP inference for image super-resolution CK Sønderby, J Caballero, L Theis, W Shi, F Huszár International Conference on Learning Representations, 2016	467	2016
Deep gaze I: Boosting saliency prediction with feature maps trained on imagenet M Kümmerer, L Theis, M Bethge arXiv preprint arXiv:1411.1045, 2014	443	2014
HoloGAN: Unsupervised learning of 3D representations from natural images T Nguyen-Phuoc, C Li, L Theis, C Richardt, YL Yang Proceedings of the IEEE International Conference on Computer Vision, 7588-7597, 2019	395	2019
Fast face-swap using convolutional neural networks I Korshunova, W Shi, J Dambre, L Theis Proceedings of the IEEE international conference on computer vision, 3677-3685, 2017	356	2017
Generative image modeling using spatial LSTMs L Theis, M Bethge Advances in Neural Information Processing Systems 28, 2015	217	2015
Benchmarking spike rate inference in population calcium imaging L Theis, P Berens, E Froudarakis, J Reimer, MR Rosón, T Baden, T Euler, ... Neuron 90 (3), 471-482, 2016	209	2016
Faster gaze prediction with dense networks and Fisher pruning L Theis, I Korshunova, A Tejani, F Huszár arXiv preprint arXiv:1801.05787, 2018	178	2018
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize A Aitken, C Ledig, L Theis, J Caballero, Z Wang, W Shi arXiv preprint arXiv:1707.02937, 2017	163	2017
Is the deconvolution layer the same as a convolutional layer? W Shi, J Caballero, L Theis, F Huszar, A Aitken, C Ledig, Z Wang arXiv preprint arXiv:1609.07009, 2016	157	2016
Super-resolution using a generative adversarial network W Shi, C Ledig, Z Wang, L Theis, F Huszar US Patent App. 15/706,428, 2018	116	2018
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data P Berens, J Freeman, T Deneux, N Chenkov, T McColgan, A Speiser, ... PLoS computational biology 14 (5), e1006157, 2018	112	2018
Training end-to-end video processes Z Wang, RD Bishop, F Huszar, L Theis US Patent 10,666,962, 2020	90	2020
Proceedings of the IEEE conference on computer vision and pattern recognition C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ... Photo-realistic single image super-resolution using a generative adversarial …, 2017	87	2017
Universally Quantized Neural Compression E Agustsson, L Theis Advances in Neural Information Processing Systems, 2020	49	2020
In All Likelihood, Deep Belief Is Not Enough L Theis, S Gerwinn, F Sinz, M Bethge Journal of Machine Learning Research 12, 3071-3096, 2011	44	2011
Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification L Theis, AM Chagas, D Arnstein, C Schwarz, M Bethge PLoS computational biology 9 (11), e1003356, 2013	41	2013
Addressing delayed feedback for continuous training with neural networks in CTR prediction SI Ktena, A Tejani, L Theis, PK Myana, D Dilipkumar, F Huszár, S Yoo, ... Proceedings of the 13th ACM conference on recommender systems, 187-195, 2019	40	2019

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