Implementation of Neural Processes in Pytorch and synthetic examples including:
- 1D regression of sin functions
- 1D regression of discontinous sin functions
- 1D regression of Gaussian Process functions
Conditional Neural Processes: Garnelo M, Rosenbaum D, Maddison CJ, Ramalho T, Saxton D, Shanahan M, Teh YW, Rezende DJ, Eslami SM. Conditional Neural Processes. In International Conference on Machine Learning 2018.
Neural Processes: Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D.J., Eslami, S.M. and Teh, Y.W. Neural processes. ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models 2018.
Attentive Neural Processes: Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O. and Teh, Y.W. Attentive Neural Processes. In International Conference on Learning Representations 2019.
- Official Neural Process implementations by Deepmind
- Emilien Dupont Neural Processes implementation
- Kaspar Märtens blogpost and R implementation
- Provides intuition for priors in NPs.
- Wessel Brunsima neuralprocesses package and documentation
- Yann Dubois et al. blog/website
- Xuesong Wangs' implementation
- Infinite Norm blogpost contrasting NPs and VAEs and PyTorch implementation
- Implementation not consistent with papers.
- VAEs learn the a latent representation for data points. NPs learn a latent representation for functions via data sets.