Reference Papers
- Correlational Neural Networks
- Optimizing Neural Networks that Generate Images (github.com/mrkulk/Unsupervised-Capsule-Network)
- Auto-Encoding Variational Bayes
- Analyzing noise in autoencoders and deep networks
- MADE: Masked Autoencoder for Distribution Estimation (github.com/mgermain/MADE)
- Winner-Take-All Autoencoders (github.com/stephenbalaban/convnet)
- k-Sparse Autoencoders (github.com/stephenbalaban/convnet)
- Zero-bias autoencoders and the benefits of co-adapting features
- Importance Weighted Autoencoders (github.com/yburda/iwae)
- Generalized Denoising Auto-Encoders as Generative Models
- 'Marginalized Denoising Auto-encoders for Nonlinear Representations'
- Real-time Hebbian Learning from Autoencoder Features for Control Tasks
- Procedural Modeling Using Autoencoder Networks (pdf) (youtu.be/wl3h4S1g2u4)
- Is Joint Training Better for Deep Auto-Encoders?
- Towards universal neural nets: Gibbs machines and ACE
- Transforming Auto-encoders
- Discovering Hidden Factors of Variation in Deep Networks