Publications of M Bethge

Meeting Abstract (23)

121.
Meeting Abstract
Bethge, M.: Factorial coding of natural images: How effective are linear filters in removing higher-order dependencies? In 7th Conference of Tuebingen Junior Neuroscientists (NeNa 2006), p. 17. 7th Conference of Tuebingen Junior Neuroscientists (NeNa 2006), Oberjoch, Germany, November 26, 2006 - November 28, 2006. (2006)
122.
Meeting Abstract
Gerwinn, S.; Seeger, M.; Zeck, G.; Bethge, M.: Bayesian Neural System identification: error bars, receptive fields and neural couplings. In 7th Conference of Tuebingen Junior Neuroscientists (NeNa 2006), p. 9. 7th Conference of Tuebingen Junior Neuroscientists (NeNa 2006), Oberjoch, Germany, November 26, 2006 - November 28, 2006. (2006)

Talk (20)

123.
Talk
Higgins, I.; Konkle, T.; Bethge, M.: What sorts of cognitive or biological (architectural) inductive biases will be crucial for developing effective artificial intelligence? NeurIPS 2019 Workshop: Shared Visual Representations in Human and Machine Intelligence, Vancouver, BC, Canada (2019)
124.
Talk
Bethge, M.: Wie Maschinen lernen: Deep Learning. HumanIThesia-Kongress „Ethik und KI“ 2017, Tübingen, Germany (2017)
125.
Talk
Bethge, M.: When will signatures of criticality provide critical insights about neural computations? Excellence Workshop "Dynamical Network States, Criticality and Cortical Function", Delmenhorst, Germany (2017)
126.
Talk
Bethge, M.: Understanding Complex Neural Network Computations. AREADNE 2016: Research in Encoding And Decoding of Neural Ensembles, Santorini, Greece (2016)
127.
Talk
Bethge, M.: Let's compete: Benchmarking models in neuroscience. NIPS 2015 Workshop on Statistical Methods for Understanding Neural Systems, Montréal, Canada (2015)
128.
Talk
Bethge, M.: Perceiving Neural Networks. Max Planck ETH Center for Learning Systems Inauguration, Tübingen, Germany (2015)
129.
Talk
Bethge, M.: Understanding biological and artificial neural networks. 5th Bernstein Sparks Workshop: Neural models of decision making in natural inference tasks - from theory to experiment, Tübingen, Germany (2015)
130.
Talk
Bethge, M.: Natural image statistics neural representation learning. Autonomous Learning: Summer School 2014, Leipzig, Germany (2014)
131.
Talk
Bethge, M.: Normative models and identification of nonlinear neural representations. Institut d'Etudes de la Cognition (IEC) at the Ecole Normale Supérieure: Group for Neural Theory, Paris, France (2013)
132.
Talk
Bethge, M.: Beyond GLMs: A generative mixture modeling approach to neural system identification. Columbia University: Workshop on Quantifying Structure in Large Neural Datasets, New York, NY, USA (2013)
133.
Talk
Bethge, M.: The unsolved mystery of neural information processing: taming the curse of dimensionality. Osnabrück Computational Cognition Alliance Meeting on "The Brain as an Information Processing System" (OCCAM 2012), Osnabrück, Germany (2012)
134.
Talk
Bethge, M.: Natural image statistics, 85% explained. City University of New York: Initiative for the Theoretical Sciences, New York, NY, USA (2012)
135.
Talk
Bethge, M.: An analytically tractable model of neural population activity in the presence of common input explains high-order correlations and entropy. CIN Systems Neuroscience Retreat, Reutlingen, Germany (2010)
136.
Talk
Bethge, M.: How Much More Does V1 Know About the Statistics of Natural Images Than the Retina? Workshop on Machine Learning: Approaches to Representational Learning and Recognition in Vision, Frankfurt a.M., Germany (2008)
137.
Talk
Macke, J.; Opper, M.; Bethge, M.: How pairwise correlations aect the redundancy in large populations of neurons. Bernstein Symposium 2008, München, Germany (2008)
138.
Talk
Bethge, M.: Hoch much can bandpass filtering, orientation selectivity, and divisive normalization contribute to the reduction of redundancy in natural images? International Workshop: Aspects of Adaptive Cortex Dynamics, Delmenhorst, Germany (2008)
139.
Talk
Bethge, M.: Sensory coding of natural images: bandpass filtering, orientation selectivity and contrast gain control. Gordon Research Conference: Sensory Coding & The Natural Environment 2008, Lucca, Italy (2008)
140.
Talk
Bethge, M.: Bayesian Inference of Neural Population Codes with a Sparsity Prior. Bernstein Center for Computational Neuroscience: Bernstein Colloquium, München, Germany (2007)
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