Hi @jbed,
I have sent an email to you, as my colleague and I are in the space of assisting people to progress through the interview process.
Cheers, Martin
Readit Newsextremelearning commented on Seeking a Partner for Senior Level Data Science Mock Interviews · Posted by u/jbed
extremelearning commented on Ask HN: Could you share your personal blog here? · Posted by u/revskill
www.extremelearning.com.au
Posts about interesting extensions I’ve found to some statistical algorithms for use in computer graphics, physics and applied maths.
Major focus on quasirandom sampling.
extremelearning commented on · Posted by u/vardocteitum
Hi,
I'm happy to chat with your brother, to help him work out what are some good questions to ask, when looking for the best kind of service provider / consultant is best for him. I might then either be able to help him myself, or I recommend someone from my network, or send a wider call on my linkedin profile for someone to help him.
martin @ robertsanalytics.com https://www.linkedin.com/in/martinroberts/
extremelearning commented on A New Coefficient of Correlation arxiv.org/abs/1909.10140... · Posted by u/malshe
The right place to add it would be here AFAICT: https://en.wikipedia.org/wiki/Correlation#Other_measures_of_.... The article on Pearson's shouldn't get sidetracked by discussing other types of variables. (or rather, it already has too many)
But the part that one would add would not necessarily be the definition of the coefficient ξn, but rather the interesting discussion at the beginning about what makes for a good correlation coefficient.
thanks. this is what i was intending to say, but you said it much better. :)
extremelearning commented on A New Coefficient of Correlation arxiv.org/abs/1909.10140... · Posted by u/malshe
This tweet contains a visualization comparison between linear correlation and “Chatarjee correlation:”
This new coefficient of correlation is really really awesome, and this visualization shows its value in such a beautifully simple presentation.
It would be great if someone who has Wikipedia edit privileges, can edit the Wikipedia article at [1] to describe/link how the Chatarjee's correlation coefficient solves many of the known limitation of Pearson's correlation coefficient. ;)
[1] https://en.wikipedia.org/wiki/Pearson_correlation_coefficien...
(especially second top-left diagram)
extremelearning commented on A New Coefficient of Correlation arxiv.org/abs/1909.10140... · Posted by u/malshe
My understanding is that one of the major critiques of statistics, especially its use in psychology, has been the use of models which are derived from the mean.
There are inherent flaws/assumptions to this approach which Peter Molenaar has done extensive work to critique (See Todd Rose's book on the subject). For anyone who understands the technique presented in this paper, does it also depend on the mean as a model like when calculating Pearson's r?
This is an order-based algorithm, so it is more related to the median than the mean.
Another very useful consequence of being order-based, is that this new coefficient is much more robust to noise/outliers than the canonical correlation coefficient.
Quite cool how the author picks a topic, explains it well, and on top of that presents some novel results. He did this before with his article on minimum-discrepancy sequences, which is one of my favourite results in math.
thank you for these kind words. ;)
Can you explain the notation [0,1)^2 unit square, does the 2 represent the spatial dimensionality? So,[0,1)^3 is the unit cube? Why is 0 inclusive, but the 1 is exclusive?
"The first is that this mapping is area-preserving, not distance-preserving." Which area is being preserved?
Is there a volume preserving choice function?
What are points t0 and t3, are those the location of the singularity points? What is the definition of those "singularity points"? Is it that seeming void in the center of the fibonacci spiral? And that void doesn't exist within the unit square case?
I especially enjoyed footnote #1.
1. yes, the index represents the dimensionality
So[0,1)^1 is a line interval, [0,1)^2 is a unit square and [0,1)^3 is the unit cube, and [0,1]^d is a d-dimensional cube.
2.Only one boundary can be included
It includes 0 but not 1 because it can only the context is usually that practitioners want a region where one edge will wrap to the opposite edge. Thus they treat [0,1)^2 as if it is actually a 2-dimensional torus.
thus the the 2 boundaries acutally map to the same point, so you can only include one of them. In our case as we are using x %1 = fractional part of x, the fractional part could be 0, if x=3.0, but it could never be exactly 1.
3) the mapping from the circle to the surface of the sphere is described here https://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_p...
the entire top edge of the square maps to the north pole, and the entire bottom edge maps to the south pole.
4.) t0 is the first point, t3 is the 4-th point.
Hope that helps!
This link - http://neilsloane.com/packings/index.html#I - has dead URLs. Like this - http://www.teleport.com/~tpgettys/dodeca.gif . I specifically wanted to check where the dodecahedron comes short.
Good article, but it'll take some time to understand it. %1 is interesting, I used to use {..} for taking fractional part, %1 is intuitively easy, though not looking particularly good...
You are right. In mathematics, the traditional notation {x} represents the fractional part of x.
Regarding the two-variable function mod(x,b). Typically this is written as x (mod b) in maths, and as x%b in computing.
It is generally well known that for positive integers x and b, the output of this function is the remainder when x is divided by b.
However, what is less well-known is that if b=1, then the convention is that:
x (mod 1) = x%1 = fractional part of x.
For example, Python, Excel both implement this special convention.
Super cool stuff! Thanks for the article. I was wondering if someone had an opinion on an adjacent idea.
I've had Nash's infinitely collapsible sphere stuck in my head for some time: https://www.quantamagazine.org/mathematicians-identify-thres...
For purposes of nearest neighbors this seems like an incredibly interesting shape to inscribe into: The sphere, despite having spherical properties also maintains linear properties due to the corrugation. To me that means we can try to inscribe orthogonal properties into both of the spaces.
My understanding of these geometries isn't complex enough to make the connections, so my question is this: Do you think its feasible to use shapes with this 'corrugated' property to make better nearest neighbor compression? My intuition tells me that you can use the shape's linear nature to push apart independent components and inscribe the rest of the details into the spherical components. Or perhaps the opposite way.
Hopefully that made sense!
I don't have any intelligent comments on your question, but I wanted to say that I am a fan of Quanta magazine, but somehow had missed this really cool article. So thanks for pointing me to this fascinating field. ;)