👀

Unfortunately, this will also entail unavoidable collateral damage with people not at fault being called out.

Let's be honest, if you are looking for a community NeurIPS might be the wrong place. It's simply too big and anonymous. With anonymity comes people gaming the system and I think we should be calling out bad behavior more aggressively, eg."co-authoring" 10 papers and not contributing to peer-review.

One of the papers co-authored by her:

I think Katrien Beuls has been doing empirical research in this direction in the context of robotics.

Haha, meant the latter.

You said the naughty word🤭

The crazy thing about epicycles: at the time Kopernicus introduced his model they were far superior in terms of predictive power compared to the heliocentric model. This was still true when Kepler refined the model with ellipses. Epicycles had been extremely refined over time (capital+labor)

For machine learning this means that neural nets will keep on winning not necessarily for technical reasons but for sheer volume of investment and work hours that are being poured in their development.

Basically, every model and even the way we do science is a child of its time and has to be understood not only in a scientific context but also in a societal and economic one.

I guess all the answers above. But I meant it more in post-modernist philosophy of science way (reading Feyerabend at the moment)

TLDR; yes... well, kind of 🙃

Hmmm but many PPLs do numeric integration... and automatic differentiation is definitely not the same as numeric differentiation

Well, the only thing that matters is statistics, e.g. the mean of a distribution, or the probability that your sample will fall into a subset of the support. Hot take: only use densities to make nice plots and convince reviewer #2.