|
| queuebert wrote:
| Very excited to read this series. Semi-supervised learning seems
| currently under-appreciated, especially in medicine.
| perone wrote:
| It is actually used a lot in biomedical domain, however the
| gains a minimal, quite different in practice than what you see
| in papers.
| ska wrote:
| >Semi-supervised learning seems currently under-appreciated,
| especially in medicine.
|
| In medicine it would be appreciated more if it were more
| effective. Many times the right answer to "I don't have enough
| data to do X" is: don't do X.
|
| I'm not entirely pessimistic on this by the way, I think
| principled semi-supervised approaches are likely to work much
| better than some of the hail mary's you see people try in the
| space with transfer learning and generative models etc. But
| it's still hard, and often it just isn't going to work with the
| kind of practical numbers some people _want_ to be able to work
| with in medicine.
| queuebert wrote:
| You're not wrong. My hunch, however, is that semi-supervised
| learning will help with some human-biased priors that are
| being implicitly used.
| jamesblonde wrote:
| The abstract should read
|
| Semi-supervised learning is one candidate, utilizing a large
| amount of _un_ labeled data conjunction with a small amount of
| labeled data.
| perone wrote:
| As someone who worked with these techniques a lot in the past, I
| can say that SSL definitely makes sense in theory, but in
| practice, the gain doesn't pay off the complexity, except in rare
| cases w/ pseudo-labelling for example, which is very simple.
| Usually you tune a lot of hyperparams and tricks to make it work
| and the gain are usually minimal if you have a reasonable amount
| of labeled data.
| mkaic wrote:
| I think the part of this that surprised me the most was learning
| that Self-Teaching actually... works? Not entirely sure why, but
| my first instinct when I was first getting into AI was that
| training a model on its own predictions would just... not provide
| any benefit for some reason. Well, today I learned otherwise! I
| love being proven wrong about stuff like this.
| johnsutor wrote:
| Time and time again, this blog does not fail to impress. I
| especially liked her piece on Diffusion models from earlier this
| year; It was a very nice, simplified version of a complex topic
| that named some of the most important papers and contributions
| over the last few years. All the while, the blog wasn't overly
| simplified like other blogs seem to do all to often (not
| providing key derivations of formulas, discussing topics at a
| glance, reading more like a PR piece than an actual informational
| blog.)
| sharemywin wrote:
| Here's a list of her other interesting papers.
|
| https://lilianweng.github.io/lil-log/archive.html
| abhgh wrote:
| Agree. She had a very informative tutorial session yesterday on
| self-supervised learning at NeurIPS-2021. While I don't think
| the recording is publicly available [1], the slides are [2].
|
| [1] https://nips.cc/virtual/2021/tutorial/21895
|
| [2] https://nips.cc/media/neurips-2021/Slides/21895.pdf
| orzig wrote:
| > Time and time again, this blog does not fail to impress
|
| "This is an impressive blog" (I agree!)
|
| I just wanted to make sure everyone else glancing through gets
| your intended message because I had to read it twice
| spijdar wrote:
| Interesting, I also initially read it with a negative
| impression, e.g. "this blog constantly fails to impress me",
| even though that's the opposite of what the sentence says.
|
| Not to derail the topic, but anyone have any insight on why
| that might me? Pretty sure it's fine, idiomatic English. Am I
| just primed to expect negative criticism in HN comments? :/
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