Methods + Algorithms + Buzzwords

[dhuppenkothen]

A running list of methods + algorithms to chase up and explore:

• Hidden Markov Models: Used a lot in speech recognition + reconstruction, though they might have been replaced by autoencoders now (?). They might be useful for real-time classification tasks
• Autoencoders:
• Active Learning: The idea is to learn with feedback (this might be especially useful for classifications aimed at follow-up, where we can give feedback to the classifier whether it gave us the right thing. The ML group at TU Dortmund is currently exploring these techniques for transient classification in neutrino experiments and VHE gamma-ray telescopes.
• Generative Adversarial Networks: seem to be the ML method of the moment. They have the advantage that they can work with unlabelled data, but they might be super hard to interpret (I don't know), so perhaps not ideal for population studies.

Possibly useful papers/reviews:

• review of time series classification methods: https://link.springer.com/article/10.1007/s10618-016-0483-9
• review of early time series classification methods (esp useful for early follow-up): https://pdfs.semanticscholar.org/8b71/25459b6d9e7fbaac71a64cc7110d45d217d2.pdf
• deep learning on multivariate time series: https://link.springer.com/article/10.1007/s11704-015-4478-2
• off-the-shelf feature extraction using RNNs: https://arxiv.org/abs/1706.08838