• ☰
  • Home
  • Introduction
  • Generative models
  • Conditioning
  • Causal and statistical dependence
  • Conditional dependence
  • Social cognition
  • Interlude - Bayesian data analysis
  • Interlude - Algorithms for inference
  • Rational process models
  • Learning as conditional inference
  • Learning with a language of thought
  • Hierarchical models
  • Occam's Razor
  • Mixture models
  • Learning (deep) continuous functions
  • Appendix - JavaScript basics
  • Appendix - Useful distributions

We’ve included a selection of readings for your enjoyment. These papers span historical and recent work in neural network modeling that intersect cognitive science and generative modeling.

• For an intuitive explanation of Variational Autoencoders check out this fairly quick tutorial.

• If you liked the above, check out this important paper on what makes VAEs work by Kingma & Welling (2013).

• See even more recent work examining VAE models that can incorporate multiple modalities Wu & Goodman (2018).

• Here’s a Bayesian view of neural network models as presented in Ch. 5 Mackay’s “Information Theory, Inference and Learning Algorithims” textbook.

• For the brave, have a look at Radford Neal’s original thesis on Bayesian neural networks.

• If you like the historical stuff, check out the origins of the backprop algorithm in this 1986 Nature paper, Rumelhart et al. (1986).

• For some, one Rumelhart paper is not nearly enough – check out this early work examining representation learning in autoencoders Rumelhart & Todd (1991).