Had this same exact thought as an undergrad like 3 years ago! I kinda gave up due to the massive barrier and difficult financial burdens faced by phd students. This feels nice to know i wasn't crazy.
I don't disagree with you on the potential financial burdens faced by PhD students but, in this case, and if I haven't missed anything, the infrastructural barrier is not high:
> All experi-ments use a single thread on a Macbook Pro with a 2.6GHz Intel Core i7-4960HQ processor. Unless stated otherwise, all timing results use five trials, with each trial reporting the fastest among 20 executions.
It may also be worth nothing that, even as a hobby, one can actually do a lot with modern hardware if you scope your project well. There is also a lot of free resources such as CoLab available to those who have very limited computing power at home/work.
Last but not least, there is also nothing stopping you from announcing your results on arXiv and, if you can be bothered (as a hobbyist), get it published in a peer-reviewed journal.
So if you still have ideas, I encourage you to go ahead and try them! :)
I see where you are coming from, but I personally don't find the comment narcissistic: I read it the OP of the comment thinking out load, saying "ah! It's nice to know that I have thought about something similar, and someone made the effort to show that it works!".
Not sure how they did it, but basically i thought about reducing things that gained complexity through layers with neural nets. Computer architecture and the layers of complexity between groups of system instructions and function code for example. There have got to be inefficiencies that can be collapsed with the help of ml.
In my opinion, that is the thing with theoretical computer science. Many of the ideas are comprehendible. And if one has the opportunity to do the work: you may get the result.
The biggest blocker for me is usually working out how I can implement a given idea without either writing a bunch of code (I'll get bored) or trying to verify if the paper even works for my use case without doing the aforementioned. One field I pay attention to with this problem seems to be abstract interpretation, with my background at least, the methods are very theoretically clean and impressive but actually implementing these as code and knowing how to write good implementations seems to be quite obtuse.
I genuinely don't understand why we allow papers to be published on computer science, with graphs plotted of the supposed efficacy of research (i.e. not just a theoretical paper), with no code attached.
To assess MADDNESS’s effectiveness, we implemented both it and existing algorithms in C++ and Python. All of our code and raw numerical results are publicly available at https://smarturl.it/Maddness. All experiments use a single thread on a Macbook Pro with a 2.6GHz Intel Core i7-4960HQ processor.
I was trying to be clear that I was referring to papers which are talking about code they wrote. If you wrote (say) a program to predict the throughput of machine code, then I want to be able to reproduce the results you claim - thats a real example, no hint of any source yet and I've been looking.
If we can't reproduce it isn't really science. I know academics often write bad code and don't like to publish their dirty work, but the buck has to stop somewhere.
tbh I don't think most ideas in TCS are comprehensible to outsiders without spending at least a year or two building background with the state of the art. Even if you can design an algorithm, to get a good TCS paper you have to prove bounds on how well it approximates and/or on its running time.
> All experi-ments use a single thread on a Macbook Pro with a 2.6GHz Intel Core i7-4960HQ processor. Unless stated otherwise, all timing results use five trials, with each trial reporting the fastest among 20 executions.
It may also be worth nothing that, even as a hobby, one can actually do a lot with modern hardware if you scope your project well. There is also a lot of free resources such as CoLab available to those who have very limited computing power at home/work.
Last but not least, there is also nothing stopping you from announcing your results on arXiv and, if you can be bothered (as a hobbyist), get it published in a peer-reviewed journal.
So if you still have ideas, I encourage you to go ahead and try them! :)