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Can someone give a concrete example of the kind of theoretical properties they desire of ``new-style'' machine learning? The kinds of properties that ``old-styl
by puzzledobserver 8y ago
Can someone give a concrete example of the kind of theoretical properties they desire of ``new-style'' machine learning? The kinds of properties that ``old-style'' learning methods guaranteed?
People often complain about interpretability: in what sense is an SVM interpretable that a deep neural network is not?
Or is the worry about gradient descent not finding global optima? But why is the global optimum a satisfactory place to be, if the theory does not also provide a satisfactory connection between the space of models and underlying reality?
The arbiter of good theory is ultimately its ability to guide and explain practical phenomena. Which machine learning phenomena are currently most in need of theoretical elucidation?
- digitalzombie 8y agoMost machine learning algorithms that aren't statistical base doesn't give a CI. From a statistical stand point it doesn't give a sense of how good your prediction is. You can get a general sense with just CV. Also your parameter is not inferable like in statistical algorithm. This is where I see people saying Deep Learning isn't interprable and there are research into this area. If you compare time series stat forecast algorithm with deep learning you at least get a CI on stat algorithm. Randomly dropping node is pretty magic in my mind. While I don't know much about SVM I know it's mathematically proven so there should be a way to interpret SVM fitted model. I sure as hell wouldn't use ML in clinical trial for drugs. That's why biostat is a thing.
- rahimiali 8y agothis is a followup post i wrote to answer exactly your question: http://www.argmin.net/2018/01/25/optics/ http://www.argmin.net/2018/01/25/optics/