NIPS Proceedingsβ

Matthias Bethge

17 Papers

  • Accurate, reliable and fast robustness evaluation (2019)
  • Learning from brains how to regularize machines (2019)
  • Generalisation in humans and deep neural networks (2018)
  • Neural system identification for large populations separating “what” and “where” (2017)
  • Generative Image Modeling Using Spatial LSTMs (2015)
  • Texture Synthesis Using Convolutional Neural Networks (2015)
  • Training sparse natural image models with a fast Gibbs sampler of an extended state space (2012)
  • Evaluating neuronal codes for inference using Fisher information (2010)
  • A joint maximum-entropy model for binary neural population patterns and continuous signals (2009)
  • Bayesian estimation of orientation preference maps (2009)
  • Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions (2009)
  • Neurometric function analysis of population codes (2009)
  • The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction (2008)
  • Bayesian Inference for Spiking Neuron Models with a Sparsity Prior (2007)
  • Near-Maximum Entropy Models for Binary Neural Representations of Natural Images (2007)
  • Receptive Fields without Spike-Triggering (2007)
  • Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution (2002)