For what it’s worth, my second year of PhD was relatively miserable compared to my first and third.
ignore the prestige games, knuckle down and do the best work you can do- it’s more impressive if you succeed at a lower rank institution anyways, imo. And if you want that signalling power you can always plan to go for a postdoc.
Wishing you the best of luck, it can be a lonely path.
I was considering signing up today, based on this I won’t.
I have had a great experience with gemini-cli, which may interest you: it only requires a google account to setup and you get a decent chunk of gemini 2.5 pro usage for free!
Magnitude is not a dimension, it’s information about each value that is lost when you normalize it. To prove this normalize any vector and then try to de-normalize it again.
This is not quite right; you are actually losing information about each of the dimensions and your mental model of reducing the dimensionality by one is misleading.
Consider [1,0] and [x,x]
Normalised we get [1,0] and [sqrt(.5),sqrt(.5)] — clearly something has changed because the first vector is now larger in dimension zero than the second, despite starting off as an arbitrary value, x, which could have been smaller than 1. As such we have lost information about x’s magnitude which we cannot recover from just the normalized vector.
Facebook released a general purpose Segment Anything Model (SAM) recently and it's been well received in industry afaict. Might be a good place to start
They're poor at recollecting the exact descriptions of the ~150,000 ICD codes, which is why these other approaches [1,2] give the known information (codes) in some way to the model and let it do the task of _assigning_ them to discharge notes, which is the hard part of the task!.
(Disclaimer: I am an author of one of these papers)
Thanks for clarifying, that's definitely a more well thought through perspective than I gave you credit for before.
I guess I would avoid updating my beliefs too strongly when a measurement I generally find quite useful (in this case gdp per capita) does not perfectly map onto a more nuanced view of the world! As you say, it's only a single data point.
I strongly dislike arguments like this. Because you can apply them to basically any metric and I find that they are generally used to cheaply discredit a potentially interesting fact about the world without actually providing an argument that the metric in question is not a useful one to consider.
In all other contexts would you also say gdp per capita is a metric not worth considering? If not then I humbly question the value of your statement.
I've also had this thought, but found that inspecting the type shows its by default a dictionary, and that it only is interpreted as a set if you treat it as such (eg add comma-seperated elements when instantiating)