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If you think the best answer to predicting things is to just throw a neural net at it, you should probably learn more control theory. :) (Less flippantly - neu

If you think the best answer to predicting things is to just throw a neural net at it, you should probably learn more control theory. :) (Less flippantly - neural nets are mainly for the case where you can't effectively do manual feature engineering because your inputs are too varied and complex, you don't particularly care about the mathematical properties or guarantees of the solution, and where you don't even really know what the solution should look like [so are forced to randomly initialize a huge nonlinear system and optimize it until useful behaviors pop out]. Something like predicting path saturation would be far better suited to traditional approaches.)

grizzles 7y ago
I was thinking in terms of relative human effort. Adding a neural network into an open source project like bittorrent is going to be very fast to integrate and will probably offer performance that meets the satisficing criteria of being instantly better than the currently offered level of performance. Edit: By the way, I accepted your initial opinion about traditional approaches being better at face value but that was like ~45 minutes ago. After thinking about it a bit more I realized that there is alot of interesting network data that could be fed into such a NN, moving it (ding!) back into the nonlinear/complex system column. Suddenly my approach is looking competitive to also being the optimal one (excluding hybrids).
- tonyarkles 7y ago
  > I realized that there is alot of interesting network data that could be fed into such a NN Care to elaborate?
  - grizzles 7y ago
    I believe it would be an interesting exercise if you let the system decide everything, like how often the data is sampled, what information is shared and with whom, etc. Information it might share could be time of day, location, all sorts of routing data, data hashes, # of connections and host throughput rates, and so on. I just did a very quick search on arxiv about leveraging ML algorithms in software defined networking. My guess is that the starting state assumptions that are most important are whether it's a centralized (homogenous) or distributed (heterogenous) network. A fun problem.
deleted 7y ago
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grizzles 7y ago

I was thinking in terms of relative human effort. Adding a neural network into an open source project like bittorrent is going to be very fast to integrate and will probably offer performance that meets the satisficing criteria of being instantly better than the currently offered level of performance. Edit: By the way, I accepted your initial opinion about traditional approaches being better at face value but that was like ~45 minutes ago. After thinking about it a bit more I realized that there is alot of interesting network data that could be fed into such a NN, moving it (ding!) back into the nonlinear/complex system column. Suddenly my approach is looking competitive to also being the optimal one (excluding hybrids).

tonyarkles 7y ago
> I realized that there is alot of interesting network data that could be fed into such a NN Care to elaborate?
- grizzles 7y ago
  I believe it would be an interesting exercise if you let the system decide everything, like how often the data is sampled, what information is shared and with whom, etc. Information it might share could be time of day, location, all sorts of routing data, data hashes, # of connections and host throughput rates, and so on. I just did a very quick search on arxiv about leveraging ML algorithms in software defined networking. My guess is that the starting state assumptions that are most important are whether it's a centralized (homogenous) or distributed (heterogenous) network. A fun problem.

deleted 7y ago

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