PAClearner

Joined 8 years ago

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Aug 11

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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.

Comment · 8 years ago

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?

Comment · 8 years ago

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

Comment · 8 years ago

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.

Comment · 8 years ago

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.

Comment · 8 years ago

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-...

Comment · 8 years ago

Elmo is bae

Comment · 8 years ago

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

Comment · 8 years ago

freeman dyson doesn't have a PhD either jajaja

Comment · 8 years ago

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

Comment · 8 years ago

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.

Comment · 8 years ago

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.

Comment · 8 years ago

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.

Comment · 8 years ago

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