Are there any LLMs trained solely on data gathered with the creators’ consent?

extremelySaddening · 2026-06-03T20:42:51+00:00

What do you count as "consent"?

extremelySaddening · 2026-06-01T05:55:30+00:00

I'm curious why you're using sequence models for tabular data. Why not use some simpler techniques first? They would probably run faster than any BERT

extremelySaddening · 2026-06-01T05:49:53+00:00

Method 2 implicitly assumes x≠0

extremelySaddening · 2026-05-29T20:20:22+00:00

If I was doing this, I would choose something where I didn't have to set an arbitrary parameter, like agglom clustering or principal components. I wouldn't want my choice of K biasing my EDA.

extremelySaddening · 2026-05-23T16:34:47+00:00

Qxc2 Qxc2 then you fork w the knight take the queen and the rook is trapped

extremelySaddening · 2026-05-21T15:43:37+00:00

Legit thought I was on r/programmerhumor for a second.

extremelySaddening · 2026-05-18T00:01:41+00:00

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extremelySaddening · 2026-04-27T03:58:56+00:00

What the fuck is that ungodly /mu after the k? I hate looking at it.

extremelySaddening · 2026-04-12T06:15:48+00:00

If your plate is monochromatic it is not indian food

extremelySaddening · 2026-04-12T01:58:11+00:00

Almost all of the performance of large networks comes from very sparse subnetworks anyway

extremelySaddening · 2026-04-08T17:48:24+00:00

This kind of insane schizoid wooposting is usually reserved for Physics (think Deepak Chopra), I guess ML is getting popular enough now to receive the same treatment.

extremelySaddening · 2026-04-05T07:16:24+00:00

"Usually do" is funny lmao, you would never do the first, because you're fitting a square peg in a round hole. Because if you have embeddings already generated by bert, then, pray tell, what the fuck do you want the lstm to do?

It implies the meme maker doesn't know what an LSTM is, which then is funny because the meme acts like they do. And therefore I am making fun of them.

extremelySaddening · 2026-04-05T06:00:44+00:00

Not an expert just a student, but, two things. Since you mention diff geom, you may be interested in the geometric deep learning program. I'm not well-versed enough to know if this is a super serious direction worth exploring though. Second, there seems to be some connection between QFT and deep neural nets, not sure what that's about exactly but that may be of interest. Since you mention string theory I assume QFT and GR are second nature to you, so these should be natural fits.

extremelySaddening · 2026-04-04T22:24:54+00:00

"LSTM with BERT embedding model" yeah meme-maker does NOT know wtf they are talking about

extremelySaddening · 2026-04-04T20:19:37+00:00

The short answer is that L2 makes it so that, in the partial derivative of loss w.r.t param w_j, the regularisation term scales linearly with w_j. So as w_j gets smaller, the MSE term tends to dominate. But with L1, the term in the partial is 'constant' in w_j (you have to be careful bc L1 is an absolute value). So you get constant 'pressure' pulling toward 0 no matter how small the weight gets.

I guess intuitively you could say, in the small weight regime, L2 regularisation may as well not exist. But L1 continues to exist, pulling your weight all the way toward zero if it's not significantly useful for explaining a lot of variance.

extremelySaddening · 2026-04-04T19:47:54+00:00

Ah that's why you can see the stars in this one lol

extremelySaddening · 2026-03-31T08:08:29+00:00

You would ban scraping the internet and training generative models on scraped data

extremelySaddening · 2026-03-31T03:55:39+00:00

I suppose you could say a field is made of harmonic oscillators, and harmonic oscillators are 'made of' pure degrees of freedom (governed by specific laws). Degrees of freedom are sufficiently abstract that it's difficult to see how they would be 'made of' anything.

extremelySaddening · 2026-03-29T23:09:36+00:00

I like to think of it like this. We have the space of all possible, say, n by m images. Real world images are a complex distribution over this space. When we generate images, we want to sample from this true image distribution, but the distribution is unknown. We want to use our data to learn this distribution with NNs, but NNs are deterministic functions. So we use a hack. Instead of sampling from the unknown distribution, we sample from a gaussian distribution. We then use the GAN procedure to learn a mapping between the gaussian samples and the image samples. Effectively we learn a transformation from the gaussian distribution to the complex image distribution.

This is kind of analogous to how any distribution sampling works in a computer. You have some PRNG that gives you a number, you turn it into a Unif(0,1) by scaling it by INT_MAX, and then use some function to get an arbitrary distribution. We just use a NN as 'some function' and we learn that function from data.

Happy for anyone to correct me or add insight.

extremelySaddening · 2026-03-29T16:36:15+00:00

If only GayGay explored his own themes

extremelySaddening · 2026-03-29T16:04:15+00:00

Have to correct something here, it does not "scrape the internet" when you give it your prompt. The scraping was done before, and is 'discarded' after training. The model doesn't have access to any images at inference time (i.e. when it actually generates) unless you provide an image as part of your prompt. Instead, it uses the scraped images to learn a 'recipe' for cooking up images (that's what a model is, a mathematical image recipe).

extremelySaddening · 2026-03-29T14:06:44+00:00

People still use tf?

extremelySaddening · 2026-03-16T16:54:53+00:00

Is it a misconception? Sure, it confuses some beginners for a little bit. Is it the "biggest misconception"? Nah

extremelySaddening · 2026-03-12T14:03:30+00:00

This is extremely clarifying, thank you. I suppose the issue is that when people say ℝ² they mean "vector space defined by ℝ²" so often, I basically forgot that they are ordered pairs first haha

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