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davidrodord92 t1_iy6u51d wrote

I'm in ML research too.

As some suggested don't reinvent the wheel if it is something already done, as academic researcher you will never compete with industrial researchers. They have tons of programmers focusing on optimizing algorithms and libraries.

But if your research is a suggestion of new equations or schemas you should probably try in order to publish it, but if you will use a technique in some part of the work or it's an application of technique in a forgotten area usea what works and add something than differentiates your work.

For example I'm working in time series forecasting, my suggestion so far is to use MODWT + stochastic + GRU instead of EEMD + LSTM, who knows if it will work I will not implement DWT or GRU from scratch

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[deleted] OP t1_iy6v17f wrote

That’s true. I shouldn’t need to reinvent the wheel unless I feel as thought I need to develop something new. As of now, it doesn’t feel like I need to. I should continue with what I’m doing

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davidrodord92 t1_iy6vi4u wrote

You can try to experiment with different techniques and libraries until you find an interesting solution maybe in that part you need to code something new.

But focus on what's the product of your research, with low inspection form advisor as in my case, it is very easy to start flying and working in many and none things at the time

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Imperial_Squid t1_iy82578 wrote

Don't reinvent the wheel but you should absolutely know a bit about how the wheel works, that way you can better interrogate the thought process and find your project

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[deleted] OP t1_iy86jmh wrote

So what I’m doing is trying to at least understand the backbone of the some of the algorithms and methods I’m using, so I know what the package is doing etc. if asked and be able to parse the result of the package

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