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cognoboffin 5 hours ago

Doesn’t RoPE use 2D rotation matrices ?

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mathisfun123 5 hours ago | parent [-]

congrats you've found literally the only example ("the exception that proves the rule").

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bsenftner 2 hours ago | parent | next [-]

Your fixation on the rotation matrix == 3D graphics is not right

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cognoboffin 4 hours ago | parent | prev [-]

SVD, and PCA are also examples.

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mathisfun123 4 hours ago | parent [-]

there is absolutely no sense in which the SVD/PCA decomposition is just a rotation matrix. you should probably review your linear algebra textbook (hint: scaling is extremely important).

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moregrist 2 hours ago | parent | next [-]

PCA is an orthogonal transformation of the covariance matrix, so like all orthogonal transformations, it’s _literally a rotation_ in N-dimensional space.

SVD is more complex but ultimately it’s just another useful decomposition of a matrix.

I’m not sure why you’re both negative and dismissive. Transformation matrices in graphics are a good and approachable way to get used to linear transformations, which turn out to be useful pretty much everywhere.

Whether or not that helps you with ML depends more on what you’re doing in ML. FAANG doesn’t have a monopoly on ML or on interesting work in ML.

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mathisfun123 2 hours ago | parent [-]

> PCA is an orthogonal transformation of the covariance matrix

Yes you're now the second person the literally repeat the same thing I've already stated extremely clearly and succinctly: PCA is not just rotation (hint: you also need to understand covariance).

> I’m not sure why you’re both negative and dismissive. Transformation matrices in graphics are a good and approachable way to get used to linear transformations, which turn out to be useful pretty much everywhere.

I've already literally drawn the analogy/metaphor that I've drawn: if you think 2d/3d rotation matrices as they are used in graphics is any kind of introduction to the matrices in ML (modeling linear transformations or otherwise) then you're probably the type of person that believes that cash registers any kind of introduction to finance.

My point is not that hard to understand. Graphics in no way, way, shape, or form prepares you for ML. I don't understand why this is so controversial.

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moregrist 2 hours ago | parent [-]

> My point is not that hard to understand.

Have you done any serious graphics programming? Even at the OpenGL 1.x level? What you’re saying just doesn’t make sense.

Just because you’re rotating and translating things in 3-space doesn’t negate that you have a stack of transforms that relate a point in world space to one on screen space and you want to be able to project from one to the other.

Nor does it make it any easier when you need to think about how to stack transforms to achieve effects like rendering a mirror.

I honed a lot of useful practical skill with linear algebra trying to get graphics to do what I wanted. And I say this as someone who’s spent the bulk of my career using linear algebra in the context of quantum mechanics, physical simulation, and ML-adjacent areas.

	▲	mathisfun123 an hour ago \| parent [-]
		> negate that you have a stack of transforms that relate a point in world space to one on screen space and you want to be able to project from one to the other. no it doesn't "negate", it's all completely orthogonal (pun) or irrelevant. like for real just please take a look at https://docs.pytorch.org/docs/2.12/nn.html and tell me which operators you're imagining have any resemblance with typical graphics linear algebra. like when you people make such claims do you really have anything concrete in mind or just hype?

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cognoboffin 3 hours ago | parent | prev [-]

SVD is the decomposition of a matrix into two rotation matrices and a scaling matrix, by definition:

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

	▲	mathisfun123 3 hours ago \| parent [-]
		i don't understand who is having trouble reading the dialogue here you or i; > there is absolutely no sense in which the SVD/PCA decomposition is just a rotation matrix... (hint: scaling is extremely important) ... > SVD is the decomposition of a matrix into two rotation matrices and a scaling matrix, by definition: yes that's exactly what i was implying when i said SVD more than just rotation, scaling is also important. my point, which is my same original point, is that if you think learning about rotation/euler matrices is going to prepare you in any way, shape, or form for ML (vis-a-vis SVD/PCA or RoPE or anything else) you're in for a very rude awakening.