Science Memes

15438 readers

1661 users here now

Welcome to c/science_memes @ Mander.xyz!

A place for majestic STEMLORD peacocking, as well as memes about the realities of working in a lab.

Rules

Don't throw mud. Behave like an intellectual and remember the human.
Keep it rooted (on topic).
No spam.
Infographics welcome, get schooled.

This is a science community. We use the Dawkins definition of meme.

Research Committee

[email protected]

Other Mander Communities

Science and Research

Biology and Life Sciences

Physical Sciences

Humanities and Social Sciences

Practical and Applied Sciences

Memes

Miscellaneous

founded 2 years ago

MODERATORS

[email protected]

fossilesque

315

just p-hack it. (mander.xyz)

submitted 6 days ago by [email protected] to c/[email protected]

8 comments fedilink hide all child comments

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 22 points 5 days ago (1 children)

Funny thing there is actually attempts at modeling uncertainty in Deep Learning. But they are rarely used because they are either super inaccurate or have super slow convergence. (MCMC, Bayesian neural networks) The problem is essentially that learning algorithms cannot properly integrate over certainty distributions, so only an approximation can be trained, which is often pretty slow.

[–] [email protected] 4 points 5 days ago

if they existed they'd be killer for RL. RL is insanely unstable when the distribution shifts as the policy starts exploring different parts of the state space. you'd think there'd be some clean approach to learning P(Xs|Ys) that can handle continuous shift of the Ys distribution in the training data, but there doesn't seem to be. just replay buffers and other kludges.