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I ask this because I'm currently learning about Neural Networks as a subset of the machine learning algorithms

Just trying to get some intuition on what sort of problems out there are categorized as "sequential" while others "non-sequential"

Thanks :)

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  • $\begingroup$ All P-complete problems are believed to be inherently sequential, but proving as much requires separating P from NC. $\endgroup$ – Tyson Williams Dec 22 '11 at 12:34
  • $\begingroup$ The scope of cstheory is theoretical computer science. Please check the sites linked in the FAQ for general AI questions. Also please provide definitions or provide links to them so people not familiar with the topic can understand the question, see the links in the FAQ for how to write a good question. $\endgroup$ – Kaveh Dec 30 '11 at 5:05
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One kind of sequential problem is making control decisions in real time. Consider the problem of driving a vehicle. The system will have a state, you can observe some measurements, and you must make a decision. You decision generates and input which then affect the future state of the vehicle and the whole process repeats. A problem which interacts with an external entity that changes its behavior based on a decision is sequential in nature. Other examples include things like optimally managing a stock portfolio, searching for an evading opponent, optimally playing a card game.

One non-sequential problem is classifying the fruit on a scanner in a grocery store. A measurement is taken, the data is processed and and output is determined. When the output generated does not alter the nature of of future outputs, it is non-sequential.

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The most obvious example in the field of neural networks:

  • image classification is a non-sequential task
  • neural machine translation (and most other NLP problems) are sequential tasks, because the translation of a given word in a sentence does not only depend on the word itself, but also on the translation of the previous word (e.g. whether arm was interpreted as limb or as weapon).
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