simonkamronn

Supervised learning has dominated deep learning but most real world applications consist mostly of unlabelled data from sensory input (i.e. temporal). How do you see deep learning will evolve, if it will, to meet that challenge?

Neural networks are "viewed" as functions whereas recurrent neural networks are viewed as programs with an internal state that changes with time. RNN's are hence a generalisation of simple neural networks. When computation power and training becomes efficient enough, do you think all deep learning networks will evolve into recurrent models?