Publications of L Theis

Hosseini, R.; Sra, S.; Theis, L.; Bethge, M.: Statistical inference with the Elliptical Gamma Distribution. Computational Statistics & Data Analysis 101, pp. 29 - 43 (2016)

Hosseini, R.; Sra, S.; Theis, L.; Bethge, M.: Inference and mixture modeling with the Elliptical Gamma Distribution. Computational Statistics Data Analysis 101, pp. 29 - 43 (2016)

Theis, L.; Berens, P.; Froudarakis, E.; Reimer, J.; Román Rosón, M.; Baden, T.; Euler, T.; Tolias, A.; Bethge, M.: Benchmarking Spike Rate Inference in Population Calcium Imaging. Neuron 90 (3), pp. 471 - 482 (2016)

Chagas, A.; Theis, L.; Sengupta, B.; Stüttgen, M.; Bethge, M.; Schwarz, C.: Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents. Frontiers in Neural Circuits 7, 190, pp. 1 - 17 (2013)

Theis, L.; Chagas , A.; Arnstein, D.; Schwarz, C.; Bethge, M.: Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Computational Biology 9 (11), pp. 1 - 9 (2013)

Theis, L.; Hosseini, R.; Bethge, M.: Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations. PLoS One 7 (7), pp. 1 - 8 (2012)

Theis, L.; Gerwinn, S.; Sinz, F.; Bethge, M.: In All Likelihood, Deep Belief Is Not Enough. The Journal of Machine Learning Research 12, pp. 3071 - 3096 (2011)

Theis, L.; van den Oord, A.; Bethge, M.: A note on the evaluation of generative models. In: International Conference on Learning Representations (ICLR 2016), pp. 1 - 10. International Conference on Learning Representations (ICLR 2016), San Juan, Puerto Rico. (2016)

Theis, L.; Bethge, M.: Generative Image Modeling Using Spatial LSTMs. In: Advances in Neural Information Processing Systems 28, pp. 1918 - 1926 (Eds. Cortes, C.; Lawrence, N.D.; Lee, D.D.; Sugiyama, M.; Garnett, R. et al.). Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2015), Montréal, Canada. Curran, Red Hook, NY, USA (2016)

Theis, L.; Hoffman, M.: A trust-region method for stochastic variational inference with applications to streaming data. In: International Conference on Machine Learning, 7-9 July 2015, Lille, France, pp. 2503 - 2511 (Eds. Bach, F.; Blei, D.). 32nd International Conference on Machine Learning (ICML 2015), Lille, France. International Machine Learning Society, Madison, WI, USA (2015)

Sra, S.; Hosseini, R.; Theis, L.; Bethge, M.: Data modeling with the elliptical gamma distribution. In: Artificial Intelligence and Statistics, 9-12 May 2015, San Diego, California, USA, pp. 903 - 911 (Eds. Lebanon, G.; Vishwanathan, S.). 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015), San Diego, CA, USA. International Machine Learning Society, Madison, WI, USA (2015)

Kümmerer, M.; Theis, L.; Bethge, M.: Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. In: International Conference on Learning Representations (ICLR 2015), pp. 1 - 12. International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA. (2014)

Theis, L.; Sohl-Dickstein, J.; Bethge, M.: Training sparse natural image models with a fast Gibbs sampler of an extended state space. In: Twenty-Sixth Annual Conference on Neural Information Processing Systems (NIPS 2012), pp. 1133 - 1141 (Eds. Bartlett, P.; Pereira, F.; Bottou, L.; Burges, C.; Weinberger, K.). Twenty-Sixth Annual Conference on Neural Information Processing Systems (NIPS 2012), Lake Tahoe, NV, USA, December 03, 2012 - December 08, 2012. Curran, Red Hook, NY, USA (2013)

Berens, P.; Theis, L.; Stone, J.; Sofroniew, N.; Tolias, A.; Bethge, M.; Freeman, J.: Standardizing and benchmarking data analysis for calcium imaging. Computational and Systems Neuroscience Meeting (COSYNE 2017), Salt Lake City, UT, USA (2017)

Bethge, M.; Theis, L.; Berens, P.; Froudarakis, E.; Reimer, J.; Roman-Roson, M.; Baden, T.; Euler, T.; Tolias, A.: Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals. Computational and Systems Neuroscience Meeting (COSYNE 2016), Salt Lake City, UT, USA (2016)

Farzami, T.; Theis, L.; Bethge, M.: Neural Adaptation as Bayesian Inference. Bernstein Conference 2013, Tübingen, Germany (2013)

Bethge, M.; Luedtke, N.; Das, D.; Theis, L.: A generative model of natural images as patchworks of textures. Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA (2013)

Theis, L.; Arnstein, D.; Chagas, A.; Schwarz, C.; Bethge, M.: Beyond GLMs: a generative mixture modeling approach to neural sys- tem identification. Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA (2013)

Theis, L.; Arnstein, D.; Chagas, A.; Schwarz, C.; Bethge, M.: Beyond GLMs: a generative mixture modeling approach to neural system identification. Bernstein Conference 2012, München, Germany (2012)

Theis, L.; Hosseini, R.; Bethge, M.: Mixtures of conditional Gaussian scale mixtures: the best model for natural images. Bernstein Conference 2012, München, Germany (2012)