Researcher Portfolio

Theis, Lucas

Max Planck Institute for Biological Cybernetics, Max Planck Society, Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society

Researcher Profile

Position: Max Planck Institute for Biological Cybernetics, Max Planck Society

Position: Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society

Additional IDs: MPIKYB: lucas

Researcher ID: https://pure.mpg.de/cone/persons/resource/persons84256

External references

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Publications

: Berens, P., Theis, L., Stone, J., Sofroniew, N., Tolias, A., Bethge, M., & Freeman, J. (2017). Standardizing and benchmarking data analysis for calcium imaging. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2017), Salt Lake City, UT, USA. [PubMan] : Hosseini, R., Sra, S., Theis, L., & Bethge, M. (2016). Inference and mixture modeling with the Elliptical Gamma Distribution. Computational Statistics Data Analysis, 101, 29-43. doi:10.1016/j.csda.2016.02.009. [PubMan] : Hosseini, R., Sra, S., Theis, L., & Bethge, M. (2016). Statistical inference with the Elliptical Gamma Distribution. Computational Statistics & Data Analysis, 101, 29-43. [PubMan] : Theis, L., Berens, P., Froudarakis, E., Reimer, J., Román Rosón, M., Baden, T., Euler, T., Tolias, A., & Bethge, M. (2016). Benchmarking Spike Rate Inference in Population Calcium Imaging. Neuron, 90(3), 471-482. doi:10.1016/j.neuron.2016.04.014. [PubMan] : Theis, L., van den Oord, A., & Bethge, M. (2016). A note on the evaluation of generative models. In International Conference on Learning Representations (ICLR 2016) (pp. 1-10). [PubMan] : Bethge, M., Theis, L., Berens, P., Froudarakis, E., Reimer, J., Roman-Roson, M., Baden, T., Euler, T., & Tolias, A. (2016). Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2016), Salt Lake City, UT, USA. [PubMan] : Theis, L., & Bethge, M. (2016). Generative Image Modeling Using Spatial LSTMs. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28 (pp. 1918-1926). Red Hook, NY, USA: Curran. [PubMan] : Theis, L., & Hoffman, M. (2015). A trust-region method for stochastic variational inference with applications to streaming data. In F. Bach, & D. Blei (Eds.), International Conference on Machine Learning, 7-9 July 2015, Lille, France (pp. 2503-2511). Madison, WI, USA: International Machine Learning Society. [PubMan] : Sra, S., Hosseini, R., Theis, L., & Bethge, M. (2015). Data modeling with the elliptical gamma distribution. In G. Lebanon, & S. Vishwanathan (Eds.), Artificial Intelligence and Statistics, 9-12 May 2015, San Diego, California, USA (pp. 903-911). Madison, WI, USA: International Machine Learning Society. [PubMan] : Kümmerer, M., Theis, L., & Bethge, M. (2014). Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. In International Conference on Learning Representations (ICLR 2015) (pp. 1-12). [PubMan] : Chagas, A., Theis, L., Sengupta, B., Stüttgen, M., Bethge, M., & Schwarz, C. (2013). Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents. Frontiers in Neural Circuits, 7: 190, pp. 1-17. doi:10.3389/fncir.2013.00190. [PubMan] : Theis, L., Chagas, A., Arnstein, D., Schwarz, C., & Bethge, M. (2013). Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Computational Biology, 9(11), 1-9. doi:10.1371/journal.pcbi.1003356. [PubMan] : Farzami, T., Theis, L., & Bethge, M. (2013). Neural Adaptation as Bayesian Inference. Poster presented at Bernstein Conference 2013, Tübingen, Germany. [PubMan] : Theis, L., Sohl-Dickstein, J., & Bethge, M. (2013). Training sparse natural image models with a fast Gibbs sampler of an extended state space. In P. Bartlett, F. Pereira, L. Bottou, C. Burges, & K. Weinberger (Eds.), Twenty-Sixth Annual Conference on Neural Information Processing Systems (NIPS 2012) (pp. 1133-1141). Red Hook, NY, USA: Curran. [PubMan] : Bethge, M., Luedtke, N., Das, D., & Theis, L. (2013). A generative model of natural images as patchworks of textures. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA. [PubMan] : Theis, L., Arnstein, D., Chagas, A., Schwarz, C., & Bethge, M. (2013). Beyond GLMs: a generative mixture modeling approach to neural sys- tem identification. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA. [PubMan] : Theis, L., Hosseini, R., & Bethge, M. (2012). Mixtures of conditional Gaussian scale mixtures: the best model for natural images. Poster presented at Bernstein Conference 2012, München, Germany. doi:10.3389/conf.fncom.2012.55.00079. [PubMan] : Theis, L., Arnstein, D., Chagas, A., Schwarz, C., & Bethge, M. (2012). Beyond GLMs: a generative mixture modeling approach to neural system identification. Poster presented at Bernstein Conference 2012, München, Germany. doi:10.3389/conf.fncom.2012.55.00080. [PubMan] : Theis, L., Hosseini, R., & Bethge, M. (2012). Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations. PLoS One, 7(7), 1-8. doi:10.1371/journal.pone.0039857. [PubMan] : Theis, L., Gerwinn, S., Sinz, F., & Bethge, M. (2011). In All Likelihood, Deep Belief Is Not Enough. The Journal of Machine Learning Research, 12, 3071-3096. [PubMan] : Arnstein, D., Theis, L., Chagas, A., Bethge, M., & Schwarz, C. (2011). LNP Analysis of Primary Whisker Afferents. Poster presented at 12th Conference of Junior Neuroscientists of Tübingen (NeNA 2011), Heiligkreuztal, Germany. [PubMan] : Theis, L., Hosseini, R., & Bethge, M. (2011). A multiscale model of natural images. Poster presented at 12th Conference of Junior Neuroscientists of Tübingen (NeNA 2011), Heiligkreuztal, Germany. [PubMan] : Theis, L., Gerwinn, S., Sinz, F., & Bethge, M. (2010). Likelihood Estimation in Deep Belief Networks. Poster presented at Bernstein Conference on Computational Neuroscience (BCCN 2010), Berlin, Germany. doi:10.3389/conf.fncom.2010.51.00116. [PubMan]