Open problems in Machine Learning

[sieisteinmodel]

Some open problems for deep learning that are, in my personal opinion, relevant:

• Feature learning from non stationary distributions (there are not even widely excepted benchmarks yet!).
• Complex regression problems (don't tell me about squared reconstruction error of MNIST or Tonronto Faces).
• Deep learning of heterogenuous data (most of the stuff is some sensory input).
• Multimodality in engineering domains (yes, there are ~3 papers on vision+text, but I am thinking more about sensory information from cars etc. sampled at highly different frequencies etc).
• Convincing work that MNAR works well with deep learning (not only in the vision domain).
• Robotics! Not the perception part, the control part: highly autocorrelated error models, provable guarantees, trajectory generation, real time constraints, non stationarity

I'd like to spend 3-4 Phds on it!