Contact

Lucas Theis

Adresse: Spemannstr. 41
72076 Tübingen
Raum Nummer: 1.A.02

 

Bild von Theis, Lucas

Lucas Theis

Position: Diplomand  Abteilung: Alumni Bethge

I am a graduate student in the computational vision and neuroscience group of Matthias Bethge.

 

Research interests:

A lot of evidence from theoretical considerations and biological observations points to the fact that a good representation for natural images should be hierarchically organized. Yet, the best known models of natural images are based on representations which are better described as shallow. My research aims at resolving this discrepancy by developing a better understanding of the regularities found in natural images and finding strategies for exploiting these in a hierarchical manner.

Präferenzen: 
Referenzen pro Seite: Jahr: Medium:

  
Zeige Zusammenfassung

Artikel (7):

Hosseini R, Sra S, Theis L und Bethge M (September-2016) Inference and mixture modeling with the Elliptical Gamma Distribution Computational Statistics & Data Analysis 101 29–43.
Theis L, Berens P, Froudarakis E, Reimer J, Román Rosón M, Baden T, Euler T, Tolias AS und Bethge M (Mai-2016) Benchmarking Spike Rate Inference in Population Calcium Imaging Neuron 90(3) 471–482.
Hosseini R, Sra S, Theis L und Bethge M (Oktober-2014) Statistical inference with the Elliptical Gamma Distribution Machine Learning . eingereicht
Chagas AM, Theis L, Sengupta B, Stüttgen MC, Bethge M und Schwarz C (Dezember-2013) Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents Frontiers in Neural Circuits 7(190) 1-17.
Theis L, Chagas AM, Arnstein D, Schwarz C und Bethge M (November-2013) Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification PLoS Computational Biology 9(11) 1-9.
Theis L, Hosseini R und Bethge M (Juli-2012) Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations PLoS ONE 7(7) 1-8.
Theis L, Gerwinn S, Sinz F und Bethge M (November-2011) In All Likelihood, Deep Belief Is Not Enough Journal of Machine Learning Research 12 3071-3096.

Beiträge zu Tagungsbänden (6):

Theis L, van den Oord A und Bethge M (Mai-3-2016) A note on the evaluation of generative models, International Conference on Learning Representations (ICLR 2016), 1-10.
Theis L und Bethge M (2016) Generative Image Modeling Using Spatial LSTMs In: Advances in Neural Information Processing Systems 28, , Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2015), Curran, Red Hook, NY, USA, 1918-1926.
Theis L und Hoffman MD (Juli-2015) A trust-region method for stochastic variational inference with applications to streaming data, 32nd International Conference on Machine Learning (ICML 2015), International Machine Learning Society, Madison, WI, USA, 2503-2511, Series: JMLR Workshop and Conference Proceedings ; 37.
Kümmerer M, Theis L und Bethge M (Mai-8-2015) Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet, International Conference on Learning Representations (ICLR 2015), 1-12.
Sra S, Hosseini R, Theis L und Bethge M (Mai-2015) Data modeling with the elliptical gamma distribution, 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015), International Machine Learning Society, Madison, WI, USA, 903–911, Series: JMLR Workshop and Conference Proceedings ; 38.
Theis L, Sohl-Dickstein J und Bethge M (April-2013) Training sparse natural image models with a fast Gibbs sampler of an extended state space In: Advances in Neural Information Processing Systems 25, , Twenty-Sixth Annual Conference on Neural Information Processing Systems (NIPS 2012), Curran, Red Hook, NY, USA, 1133-1141.

Beiträge zu Büchern (1):

Gerhard HE, Theis L und Bethge M: Modeling Natural Image Statistics, 53-80. In: Biologically inspired Computer Vision: Fundamentals and Applications, (Ed) G. Cristóbal, Wiley-VCH, Weinheim, Germany, (2015).

Poster (9):

Berens P, Theis L, Stone J, Sofroniew N, Tolias A, Bethge M und Freeman J (Februar-23-2017): Standardizing and benchmarking data analysis for calcium imaging, Computational and Systems Neuroscience Meeting (COSYNE 2017), Salt Lake City, UT, USA.
Bethge M, Theis L, Berens P, Froudarakis E, Reimer J, Roman-Roson M, Baden T, Euler T und Tolias A (Februar-26-2016): 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.
Bethge M, Luedtke N, Das D und Theis L (März-2013): A generative model of natural images as patchworks of textures, Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA.
Theis L, Arnstein D, Maia Chagas A, Schwarz C und Bethge M (März-2013): Beyond GLMs: a generative mixture modeling approach to neural system identification, Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA.
Theis LM, Arnstein D, Chagas AM, Schwarz C und Bethge M (September-14-2012): Beyond GLMs: a generative mixture modeling approach to neural system identification, Bernstein Conference 2012, München, Germany, Frontiers in Computational Neuroscience, Conference Abstract: Bernstein Conference 2012 165.
Theis LM, Hosseini R und Bethge M (September-14-2012): Mixtures of conditional Gaussian scale mixtures: the best model for natural images, Bernstein Conference 2012, München, Germany, Frontiers in Computational Neuroscience, Conference Abstract: Bernstein Conference 2012 247.
Theis L, Hosseini R und Bethge M (Oktober-2011): A multiscale model of natural images, 12th Conference of Junior Neuroscientists of Tübingen (NeNA 2011), Heiligkreuztal, Germany.
Arnstein D, Theis L, Chagas AM, Bethge M und Schwarz C (Oktober-2011): LNP Analysis of Primary Whisker Afferents, 12th Conference of Junior Neuroscientists of Tübingen (NeNA 2011), Heiligkreuztal, Germany.
Theis L, Gerwinn S, Sinz F und Bethge M (Oktober-2010): Likelihood Estimation in Deep Belief Networks, Bernstein Conference on Computational Neuroscience (BCCN 2010), Berlin, Germany, Frontiers in Computational Neuroscience, 2010(Conference Abstract: Bernstein Conference on Computational Neuroscience).

Export als:
BibTeX, XML, pubman, Edoc, RTF
Last updated: Montag, 22.05.2017