dat_math

joined 4 years ago
[–] dat_math@hexbear.net 4 points 1 year ago (1 children)

tepary

420-69-6669

Queipo de Llano

[–] dat_math@hexbear.net 11 points 1 year ago

oops! all branches!

Why can't I hold all these continuua?!

[–] dat_math@hexbear.net 18 points 1 year ago* (last edited 1 year ago) (2 children)

It's okay. The Real numbers are dense (within the real numbers), so no matter how many branches we make, we'll always have just as many real numbers jammed arbitrarily close together on all branches!

[–] dat_math@hexbear.net 9 points 1 year ago

I buy them in bulk from home despot in amerikkka

[–] dat_math@hexbear.net 1 points 1 year ago* (last edited 1 year ago)

DAH DAH DAH

I'm the son of fabaceae

The beanis of suburbia, the legume of none of the above on a steady diet of:

DAH DAH DAH

Nitrates and urea and

compost and manure and all the nitrogen and the co2 that is in the atmospheeere

and there's nothing wrong with me

this is how I'm supposed to beeeeaaaaaaaaaan

in a land of cereal grains that don't believe in beanis

[–] dat_math@hexbear.net 3 points 1 year ago

Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean

Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean Bean

[–] dat_math@hexbear.net 30 points 1 year ago

nobody wants to work anymore

[–] dat_math@hexbear.net 20 points 1 year ago* (last edited 1 year ago)

If I used my immunity-from-contracting-and-transmitting diseases wish on myself and my partner, I would mask anyways because explaining that I don't need to wear a mask because a supernatural force granted me 3 wishes is harder than just showing people that hot gamers should mask too

[–] dat_math@hexbear.net 6 points 1 year ago* (last edited 1 year ago)

^_^ thank you!

[–] dat_math@hexbear.net 4 points 1 year ago* (last edited 1 year ago)

I like how compact this one is ;)

[–] dat_math@hexbear.net 6 points 1 year ago* (last edited 1 year ago) (3 children)

Not quite. The wording "equivalence classes of ... with respect to the relation R: aRb <==> lim( a_n - b_n) as n->inf" is key.

https://en.wikipedia.org/wiki/Equivalence_class

loosely, an equivalence relation is a relation between things in a set that behaves the way we want an equal sign to

For an element in a set, a, the equivalence class of a is the set of all things in the larger set that are equivalent to a.

 
 

No twinks, folks

20
Yellow Eye Beanis (hexbear.net)
submitted 1 year ago* (last edited 1 year ago) by dat_math@hexbear.net to c/badposting@hexbear.net
 

beanis Heirloom Beanis posting is back on the menu! beanis

 

“I dabble, but not in the way that I used to before,” she said, adding the recent waves of anti-Israel encampments at Columbia and other universities prompted brief relapses.

caseomorphins: not even once

apologies if I missed a content warning or if this kind of article is inappropriate for the comm

 

I welcome constructive critique of my theory on the classification of hummus as a smoothie

 

 

and the madkatz gamecube controller

 

Not a true beanis, but a cultivar of brassica oleracea

If you ever get the opportunity, roast the kohlrabeanis

 

How can I train my voice to sound this full and smooth?

 

so named because they look like coffee beanis

 

preprint version because scihub doesn't have it yet https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120732/

Abstract

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of the brain response associated with each sentence. We then use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress the activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also non-invasively control neural activity in higher-level cortical areas, such as the language network.

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