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The universal weight subspace hypothesis

arxiv.org

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Re: The universal weight subspace hypothesis

#111

Post by [deleted] »

rocqua wrote: post #107
bmacho wrote: post #103

Earlier quoted context omitted.

Vote for the Party that promises academic grants for people that write 1k character long forum posts for the laypeople instead of other experts of the field.

I don't think the parent post is complaining that academics are writing proposals (e.g as opposed to people with common sense). Instead, it seems to me that he is complaining that academics are writing proposals and papers to impress funding committees and journal editors, and to some extend to increase their own clout among their peers. Instead of writing to communicate clearly and honestly to their peers, or occasi…

[deleted]

Re: The universal weight subspace hypothesis

#112

Post by shoubidouwah »

bmacho wrote: post #103
swivelmaster wrote: post #101

Earlier quoted context omitted.

You’ve explained this in plain and simple language far more directly than the linked study. Score yet another point for the theory that academic papers are deliberately written to be obtuse to laypeople rather than striving for accessibility.

Vote for the Party that promises academic grants for people that write 1k character long forum posts for the laypeople instead of other experts of the field.

and hope for a president that can do both

Re: The universal weight subspace hypothesis

#113

Post by janalsncm »

topspin wrote: post #88
masteranza wrote: post #23

It's basically way better than LoRA under all respects and could even be used to speed up inference. I wonder whether the big models are not using it already... If not we'll see a blow up in capabilities very, very soon. What they've shown is that you can find the subset of parameters responsible for transfer of capability to new tasks. Does it apply to completely novel tasks? No, that would be magic. Tasks that need…

> Does it apply to completely novel tasks? No, that would be magic. Are there novel tasks? Inside the limits of physics, tasks are finite, and most of them are pointless. One can certainly entertain tasks that transcend physics, but that isn't necessary if one merely wants an immortal and indomitable electronic god.

Within the context of this paper, novel just means anything that’s not a vision transformer.

Re: The universal weight subspace hypothesis

#115

Post by markisus »

modeless wrote: post #58

This seems confusingly phrased. When they say things like "500 Vision Transformers", what they mean is 500 finetunes of the same base model, downloaded from the huggingface accounts of anonymous randos. These spaces are only "universal" to a single pretrained base model AFAICT. Is it really that surprising that finetunes would be extremely similar to each other? Especially LoRAs? I visited one of the models they refe…

Each fine tune drags the model weights away from the base model in a certain direction.

Given 500 fine tune datasets, we could expect the 500 drag directions to span a 500 dimensional space. After all, 500 random vectors in a high dimensional space are likely to be mutually orthogonal.

The paper shows, however, that the 500 drag directions live in a ~40 dimensional subspace.

Another way to say it is that you can compress fine tune weights into a vector of 40 floats.

Imagine if, one day, fine tunes on huggingface were not measured in gigabytes, megabytes, or even kilobytes. Suppose you started to see listings like 160 bytes. Would that be surprising?

I’m leaving out the detail that the basis direction vectors themselves would have to be on your machine and each basis direction is as big as the model itself. And I’m also taking for granted that the subspace dimension will not increase as the number of fine tune datasets increases.

I agree that the authors decision to use random models on hugging face is unfortunate. I’m hopeful that this paper will inspire follow up works that train large models from scratch.

Re: The universal weight subspace hypothesis

#116

Post by dcrimp »

VikingCoder wrote: post #5

I find myself wanting genetic algorithms to be applied to try to develop and improve these structures... But I always want Genetic Algorithms to show up in any discussion about neural networks...

I've been messing around with GA recently, esp indirect encoding methods. This paper seems in support of perspectives I've read while researching. In particular, that you can decompose weight matrices into spectral patterns - similar to JPEG compression and search in compressed space.

Something I've been interested in recently is - I wonder if it'd be possible to encode a known-good model - some massive pretrained thing - and use that as a starting point for further mutations.

Like some other comments in this thread have suggested, it would mean we can distill the weight patterns of things like attention, convolution, etc. and not have to discover them by mutation - so - making use of the many phd-hours it took to develop those patterns, and using them as a springboard. If papers like this are to be believed, more advanced mechanisms may be able to be discovered.

Re: The universal weight subspace hypothesis

#117

Post by api »

EvanAnderson wrote: post #13
api wrote: post #7

I immediately started thinking that if there are such patterns maybe they capture something about the deeper structure of the universe.

On a hike this weekend my daughter and I talked about the similarities of the branching and bifurcating patterns in the melting ice on a pond, the branches of trees, still photos of lightning, the circulatory system, and the filaments in fractals.

Find some images of the entire huge scale structure of the universe. It looks a bit like… a brain.

What does this mean? Probably not nothing, but probably not “the cosmos is the mind of god.” It probably means that we live in a universe that tends to produce repeating nested patterns at different scales.

But maybe that’s part of what makes it possible to evolve or engineer brains that can understand it. If it had no regularity there’d be no common structural motifs.

Re: The universal weight subspace hypothesis

#118

Post by VikingCoder »

EvanAnderson wrote: post #12
VikingCoder wrote: post #5

I find myself wanting genetic algorithms to be applied to try to develop and improve these structures... But I always want Genetic Algorithms to show up in any discussion about neural networks...

I have a real soft spot for the genetic algorithm as a result of reading Levy's "Artificial Life" when I was a kid. The analogy to biological life is more approachable to my poor math education than neural networks. I can grok crossover and mutation pretty easily. Backpropagation is too much for my little brain to handle.

In grad school, I wrote an ant simulator. There was a 2D grid of squares. I put ant food all over it, in hard-coded locations. Then I had a neural network for an ant. The inputs were "is there any food to the left? to the diagonal left? straight ahead? to the diagonal right? to the right?" The outputs were "turn left, move forward, turn right."

Then I had a multi-layer network - I don't remember how many layers.

Then I was using a simple Genetic Algorithm to try to set the weights.

Essentially, it was like breeding up a winner for the snake game - but you always know where all of the food is, and the ant always started in the same square. I was trying to maximize the score for how many food items the ant would eventually find.

In retrospect, it was pretty stupid. Too much of it was hard-coded, and I didn't have near enough middle layers to do anything really interesting. And I was essentially coming up with a way to not have to do back-propagation.

At the time, I convinced myself I was selecting for instinctive knowledge...

And I was very excited by research that said that, rather than having one pool of 10,000 ants...

It was better to have 10 islands of 1,000 ants, and to occasionally let genetic information travel from one island to another island. The research claimed the overall system would converge faster.

I thought that was super cool, and made me excited that easy parallelism would be rewarded.

I daydream about all of that, still.

Re: The universal weight subspace hypothesis

#120

Post by mapt »

bmacho wrote: post #103
swivelmaster wrote: post #101

Earlier quoted context omitted.

You’ve explained this in plain and simple language far more directly than the linked study. Score yet another point for the theory that academic papers are deliberately written to be obtuse to laypeople rather than striving for accessibility.

Vote for the Party that promises academic grants for people that write 1k character long forum posts for the laypeople instead of other experts of the field.

We have this already. It's called an abstract. Some do it better than others.

Perhaps we need to revisit the concept and have a narrow abstract and a lay abstract, given how niche science has become.

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