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This library originated from my need to handle 3D transformations, poses, trajectories, etc. from various software systems that often use different conventions. It has been developed for more than 7 years now and is in a fairly robust and well-tested state. It might be useful, for instance, in robotics, computer vision, motion capture, ...


Hi, that looks interesting. Where can I make a bug report? Can’t see the red button to start recording.


Email me at hello@keke.dev. Lets figure it out.


Great! I sent an email.


An Amazon review is not comparable to a review in a scientific journal. In a scientific journal a review is done by experts of the field and they invest a couple of hours / days. Scientists do this because it is part of their job. It is a contribution to the scientific community.


The academic reviews I write take me about one to three days of work. I have to work through a usually densely written paper, maybe read up on some background that I'm not entirely familiar with, develop a good understanding of the content, judge it, find places where the authors might try to hide some downsides of their work (it happens!) etc. Only then can I sit down and write an honest review.


Maybe someone finds this interesting. This paper seems to be related: https://arxiv.org/abs/1907.04490


I've been meaning to read this, will keep you posted


Same here. Those two books seem to be ordered only as pairs. ;)


Yes, you don't need to switch to a browser.


This reminds me a lot of the work on compressed neural network from Jan Koutnik and his colleagues. They don't evolve topology of a NN, but they learn weights of a neural network in some compressed space. That seems to be very similar to weight sharing.

Here are some related papers:

- original idea: http://people.idsia.ch/~tino/papers/koutnik.gecco10.pdf

- vision-based TORCS: http://repository.supsi.ch/4548/1/koutnik2013fdg.pdf

- backpropagation with compressed weights: http://www.informatik.uni-bremen.de/~afabisch/files/2013_NN_...

For example, in the case of the cart pole (without swing up) benchmark a simple linear controller with equal positive weights is required which can easily be encoded with this approach.


Hi,

Thanks for the references. The GECCO paper on compressed network search has been a big influence on previous projects I worked on, see:

https://news.ycombinator.com/item?id=16694153

https://news.ycombinator.com/item?id=14883694

it’s a small community!


Overfitting is a well-known problem in the ML community. There are methods to avoid this: cross validation, train-test splits, etc. There are also models that give you an estimate of the standard deviation of a prediction. What is the point? We don't need new algorithms, we just have to apply existing methods properly.


I live in Germany and I would probably just go to the next hospital, wait for a while, get x-rayed and see a doctor. In this situation I wouldn't even have to think about money and I don't have to search for the best provider because they are all good. Even if I wouldn't go to a hospital, every doctor who can treat you usually can do an x-ray at his practice. I cannot imagine how it must feel in such a system in urgent cases. (edit: "such a system" refers to the American system)


There is Jodel: https://jodel-app.com/


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