Bias and Generalization in Deep Generative Models: An Empirical Study

M. Rosenblatt, “Remarks on some nonparametric estimates of a density function,” The Annals of Mathematical Statistics

1956

Earlier work this paper cites.

M. Rosenblatt, “A central limit theorem and a strong mixing condition,” Proceedings of the National Academy of Sciences

1956

Earlier work this paper cites.

Department of Computer Science, Laboratory for Computer Science Research, Rutgers Univ. New Jersey, 1980

T. M. Mitchell, The need for biases in learning generalizations · 1980

Earlier work this paper cites.

D. J. Smith and J. P. Minda, “Thirty categorization results in search of a model.,” Journal of Experimental Psychology: Learning, Memory, and Cognition

2000

Earlier work this paper cites.

A. Nieder, D. J. Freedman, and E. K. Miller, “Representation of the quantity of visual items in the primate prefrontal cortex,” Science

2002

Earlier work this paper cites.

A. Nieder and E. K. Miller, “Coding of cognitive magnitude: Compressed scaling of numerical information in the primate prefrontal cortex,” Neuron

2003

Earlier work this paper cites.

M. Piazza, V. Izard, P. Pinel, D. Le Bihan, and S. Dehaene, “Tuning curves for approximate numerosity in the human intraparietal sulcus,” Neuron

2004

Earlier work this paper cites.

A. Gretton, K. M. Borgwardt, M. Rasch, B. Schölkopf, and A. J. Smola, “A kernel method for the two-sample-problem,” in Advances in neural information processing systems

2007

Earlier work this paper cites.

A. Nieder and K. Merten, “A labeled-line code for small and large numerosities in the monkey prefrontal cortex,” Journal of Neuroscience

2007

Earlier work this paper cites.

S. Efromovich, “Orthogonal series density estimation,” Wiley Interdisciplinary Reviews: Computational Statistics

2010

Earlier work this paper cites.

G. A. Alvarez, “Representing multiple objects as an ensemble enhances visual cognition,” Trends in cognitive sciences

2011

Earlier work this paper cites.

J. P. Minda and J. D. Smith, “Prototype models of categorization: Basic formulation, predictions, and limitations,” Formal approaches in categorization

2011

Cited alongside, same era.

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114

Original

2013

Cited alongside, same era.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems

2014

Cited alongside, same era.

D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” arXiv preprint arXiv:1401.4082

Original

2014

Cited alongside, same era.

L. Theis, A. v. d. Oord, and M. Bethge, “A note on the evaluation of generative models,” arXiv preprint arXiv:1511.01844

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-vae: Learning basic visual concepts with a constrained variational framework,” 2016

Original

2016

Later among the works it cites.

S. Arora and Y. Zhang, “Do gans actually learn the distribution? an empirical study,” arXiv preprint arXiv:1706.08224

Original

2017

Later among the works it cites.

Routledge, 2017

S. S. Stevens, Psychophysics: Introduction to its perceptual, neural and social prospects · 2017

Later among the works it cites.

S. Zhao, J. Song, and S. Ermon, “Learning hierarchical features from deep generative models,” in International Conference on Machine Learning

2017

Later among the works it cites.

Y. Li, J. Song, and S. Ermon, “Infogail: Interpretable imitation learning from visual demonstrations,” in Advances in Neural Information Processing Systems