Short answer: if the inputs can be represented well on the Fourier basis, yes. I have a patent in process on this, fingers crossed.
Longer answer: deep learning models are usually trying to find the best nonlinear basis in which to represent inputs; if the inputs are well-represented (read that as: can be sparsely represented) in some basis known a-priori, it usually helps to just put them in that basis, e.g., by FFT’ing RF signals.
The challenge is that the overall-optimal basis might not be the same as those of any local minima, so you’ve got to do some tricks to nudge the network closer.
*can manage on its own well. The market manages - explicitly or through regulatory capture and toadies - a hell of a lot of things that the state probably should, like the Texas power grid. Markets are rarely efficient except in the very short term and very small scale.
No, this is only true if that hyperplane contains the origin; imagine an infinite number of hyperplanes that contain A and B; there are an infinite number of such planes for dimensions higher than 2. Now imagine the same, but connecting O and C; most of those AB hyperplanes are not orthogonal to those OC hyperplanes, it’s only coincidence if they are, though for dimensions higher than 2 you can always find a point C that happens to lie along the orthogonal line from the AB plane to the origin.
- This thread is about marketing. Did you do all of the marketing, or did an existing infrastructure perform tracking and serve ads for you?
- What data support claims about your users’ experience? Conversions are not a good metric of user experience.
- People generally have a hard time evaluating the ethical merits of things that benefit them. Do you have some kind of independent evaluation so support your claim that you did nothing unethical? If a politician hires a lawyer as a fixer, and pays them to make problems go away with a minimum of information returned, is that politician acting ethically? If the fixer hires a hitman for that problem, does the politician’s ignorance of that act constitute ethical impunity?
I’m mostly a radar guy so I’m missing it — is it the fact that we only see the upper sideband, as though the signal is analytic? I don’t know this software (only a bit of GNUradio now and then) so I don’t know if the green bar up top is just the filtered passband or what. If it’s supposed to be the passband, I get that some of the spectra (“demo2,” for example) creep out of it, which is fishy. Any insight is appreciated!
This doesn’t apply to folks who want contacts for what’s in their profile, but: sometimes you’d like employers to know what relevant adjacent skills you have, without recruiters hitting them as search terms.
For that purpose, I masked all of my software terms like “java” from my EE profile with homoglyphs: humans can read them, but they don’t match search terms.
Longer answer: deep learning models are usually trying to find the best nonlinear basis in which to represent inputs; if the inputs are well-represented (read that as: can be sparsely represented) in some basis known a-priori, it usually helps to just put them in that basis, e.g., by FFT’ing RF signals.
The challenge is that the overall-optimal basis might not be the same as those of any local minima, so you’ve got to do some tricks to nudge the network closer.