PAClearner

ummm you are probably wrong. those job postings probably want people with PhDs in Computer Science/Statistics/Applied Math that did hard things with data.

maybe i am just tired right now but "average of sums is equal to the sum of averages"

is basically true 100% of the time-it is called the linearity of expectation and is a basic fact of probability.

Could you include the papers or provide a more detailed comment?

I don't know if people do this for vision yet but I know some people doing this for RL for robotic control.

andrej karpathy is kinda pretty freaking legit but I think the waymo team is probably stronger/larger and has significantly more engineering maturity.

caveat: I don't do vision.

for anyone who maybe got confused SLAM stands for Simultaneous Localization and Mapping.

edit: I think if it is possible to do SLAM without LIDAR waymo will definitely beat TESLA to the punch since waymo has an absurd amount of vision data labelled with the lidar-measured-depth.

Reading a full unedited transcription of a speech from a president is also pretty direct...

that's existed for 150 years at least

i.e. https://yorkblog.com/yorkspast/read-the-actual-article-next-...

Elmo is bae

you must do theory right? tcs? based on the name edit: AI and ML are close enough :)

freeman dyson doesn't have a PhD either jajaja

we call them hidden layers because of path dependence i.e. that is what people called them at first.

the first neural network-like things would have input nodes directly connected to output nodes. Thus both layers were 'exposed' and visible. The hiddden layers are 'hidden' because you don't directly touch them or use them-they are obscured. You can call them intermediate layers if you want I suppose-people will probably understand what you mean

https://arxiv.org/list/cs.LG/recent

from there I look for papers

1. Immediately relevant to my current interests 2. by people I who know write good papers.

For chaff-vs-wheat some of my friends use arxiv sanity?

to find popular articles I go to -google scholar page of someone well known for a topic and look at their highly cited papers -look at citations of highly cited papers

edit: just a warning-short term popularity of a scientific paper is almost surely not a good indicator of long term value.

1. arxiv 2. nytimes [trying to quit] 3. reddit [trying to quit] 4. hackernews [trying to quit]

tbh I really like trying to minimize my browsing in the morning.

100% correct. I think we are both very lucky in that we are able to do fun science and chase our intellectual interests with a realistic and still fun safety net.

Also those closet programmers are always really fun to talk to since their problems and culture are breath of fresh air.

I could rant about this for hours. I actually just went to a defense for a deep learning paper that had a ton of abstract algebra. I am honestly not really a fan of deep learning and algebra because all the papers to me like- seem to stop at describing some really basic feedforward network as some really specific mathematical structure but these theories a) provide very little explanation of empirical phenomena b) provide no new directions of research in terms of like useful network architectures.

I haven't really come across algebra in machine learning other than people applying it to deep learning.

i.e. https://arxiv.org/pdf/1802.03690.pdf

ie. https://icml.cc/Conferences/2018/Schedule?showEvent=2048

I don't personally find papers like this valuable but idk I have never really enjoyed abstract algebra.

For areas of mathematics to do theory in ML (and to do ML more generally!)

-probability/concentration/hoeffding bounds [the PAC model] [Key]

-linear algebra [key]

-optimization [key]

for books

-understanding machine learning by shai ben david

This book is nice since it really balances theory with a more practical understanding.

-An Introduction to Computational Learning Theory by kearns is a classic [low priority]. this is fun since the proofs are simple and deep but is very very far away from practical algorithms.

-convex optimization by boyd

Course Notes:

[I think a good alternative to blogs is stalking course notes for other schools-they are very often public.]

- http://ttic.uchicago.edu/~avrim/MLT18/index.html

good learning theory course by avrim blum who is a big deal in learning theory and theory.

- tim roughgardens notes are a blessing for algorithms and theory [seriously he should have a patreon or something]

https://theory.stanford.edu/~tim/notes.html

Blogs:

-http://www.argmin.net/

this is ben recht's blog and is filled with ML wisdom.

-https://blogs.princeton.edu/imabandit/ not quite learning theory but a lot of ML adjacent stuff

I don't read many blogs as I should tbh so other people can give better advice

VIDEOS https://www.youtube.com/channel/UCW1C2xOfXsIzPgjXyuhkw9g

This is the simons institute youtube channel. probably the best single location for recordings of TALKS in computer science-good amount of ML talks.

https://simons.berkeley.edu/videos

Well I would be happy to provide some context. I just finished my first year of CS Phd in ML (more on the theory side) and I really like it. I think most of the places you would want to do a post doc in CS are probably going to be moderately high CoL. My phd is in a place with pretty low CoL (but a still a top 10-top 20 school (depending on who you ask) ) so the graduate stipend goes reasonably far.

The other thing to note in ML is that it seems like a few people go to industry research labs for a few years i.e MSR/FAiR/google brain and then come back to the academy since there are industry roles that involve research and publication. for instance moritz hardt.

my personal plan for the first 3 years of grad school is to work really hard and try to keep both academia and industry open and after year 3 evaluate the number of publications I have and my current skill set to see if I can make it in the academy or shift more towards industry.

I think the biggest factor I would comment on is look very closely about what jobs the graduated students from the department you matriculate at AND more importantly the professor you want to work with go on to do post Phd. There are a lot of naysayers in this thread about the risks of an academic career and I share those concerns but I felt a lot more comfortable taking the plunge after I looked at the career record of the graduated students of my advisor. They were all either tenure track or had good industry positions.

edit: if your advisor has collaborators in industry groups I think it is pretty straight forward to get an industry gig.

being around really smart people doing interesting things is pretty great.

are you already a grad student or are you considering it?

in my department [we are an outlier probably] post doc wages are actually pretty comparable to industry i.e. >= 100k. 40k to 70k seems low to me.

I would expect the range to be more like 60-100k.

I haven't been on the real job market yet-but the key to getting a TT or postdoc position seems to be a) collaborators who want to hire you b) people who have heard you give a talk in person and are familiar with your research.

And a blockchain would be a way to do it

I can't even.

meh as a baby academic I am not convinced that the nepotism protectionism elitism of academia are markedly higher than the 'real world'

you should be more specific-what part of CS?

all the CS postdocs I know see a) pretty happy b) have a really easy back up of a high paying industry gig that they can pivot to because of currently insanely inflated demand for academics to make deep learning on the blockchain on the cloud on embedded devices doing quantum backprop

-these are ML/theory postdocs

while there are good parts to this analysis -

military grade tech <---> wildly over-budget and delayed

did you read the article? she clearly suffered severe damage.

woah-also this really like-viscerally demonstrates the destructive power of rifles.

1. I am also uncomfortable about this

I was thinking more co-authorship but I guess the parent used 'linked' more properly as 'advised'

so 401k for tax-sheltered institutions? nifty!

I was sorta thinking about like raw empirical impact ya know? like normative vs descriptive.

idk man they are hugely famous in computer science and are also both renowned for being very kind and well known in the community. they aren't randos getting in a fight with their department. they are academic royalty so their actions are by default assigned a lot of credibility.

as a someone in graduate school for computer science [not cmu] I find this shocking and am INTENSELY curious about the details. hard to imagine a more effective resignation.

imagine if steve jobs resigned from apple because the boards was being sexist or something-I think that resignation would get a LOT of attention. that might be sorta similar to this situation.

lots of rock stars also linked to Professor Lenore Blum too! [I am sure you know this I just wanted to make note of it for the general public]

think 'quitting' vs 'retiring'. no idea about pensions etc. I would be surprised if CMU offers pensions and not a normal 401k.