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I expect you'll run into two challenges in this line of research: (1) there are no known ways to construct a BNN that exactly matches an ordinary neural network; (2) verifying a BNN with a SAT solver won't scale to the size models needed for most interesting tasks. Since you asked specifically about (1), no, I don't know of any way to do that. Probably you ...


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The energy usage will vary depending on the machine, but as long as all of your results are from the same setup there's a comparatively low-tech, simple solution: Hook up your computer to an electricity meter and take some measurements with the computer "at rest", to get a baseline of its energy consumption. Then, run several trials with each model you're ...


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(Not enough reputation to comment, so writing here.) Unless your algorithms are secret, please post your algorithms here. Maybe someone (not me though) can find a library for you. Maybe someone can tell how long does it take to implement it. Use Git and GitHub. You can rollback bad code with this. Always write tests. This helps against regressions as you ...


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I have been implementing a branch and bound solver with heuristics for an NP-hard problem. It got complicated at some points and had to reimplement parts a couple of times. The problem was (I think), that I started implementing with only an intuition about the design and how it looks like. That is bad software engineering and is catastrophic in big project. ...


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some says it is because of this architecture SVM is comparable to neural nets


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Well, first let's clarify the following. Asymptotic Complexity and real life situations are different. Please take a look at this Question. Now, machine learning is a very hard topic to explore precisely the asymptotic complexity of your algorithms. If we think machine learning as linear algebra, then the asymptotic complexity is the complexity of ...


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