Search results

Journal Article (7)

  1. 1.
    Journal Article
    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)
  2. 2.
    Journal Article
    Hosseini, R.; Sra, S.; Theis, L.; Bethge, M.: Statistical inference with the Elliptical Gamma Distribution. Computational Statistics & Data Analysis 101, pp. 29 - 43 (2016)
  3. 3.
    Journal Article
    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)
  4. 4.
    Journal Article
    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 (2013)
  5. 5.
    Journal Article
    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) (2013)
  6. 6.
    Journal Article
    Theis, L.; Hosseini, R.; Bethge, M.: Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations. PLoS One 7 (7) (2012)
  7. 7.
    Journal Article
    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)

Conference Paper (6)

  1. 8.
    Conference Paper
    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)
  2. 9.
    Conference Paper
    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)
  3. 10.
    Conference Paper
    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)
  4. 11.
    Conference Paper
    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)
  5. 12.
    Conference Paper
    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)
  6. 13.
    Conference Paper
    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)

Poster (10)

  1. 14.
    Poster
    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)
  2. 15.
    Poster
    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)
  3. 16.
    Poster
    Farzami, T.; Theis, L.; Bethge, M.: Neural Adaptation as Bayesian Inference. Bernstein Conference 2013, Tübingen, Germany (2013)
  4. 17.
    Poster
    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)
  5. 18.
    Poster
    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)
  6. 19.
    Poster
    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)
  7. 20.
    Poster
    Theis, L.; Hosseini, R.; Bethge, M.: Mixtures of conditional Gaussian scale mixtures: the best model for natural images. Bernstein Conference 2012, München, Germany (2012)
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