[HN Gopher] Parametric Matrix Models
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Parametric Matrix Models
Author : evanb
Score : 55 points
Date : 2024-07-16 13:48 UTC (4 days ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| cs702 wrote:
| Huh, interesting.
|
| According to the authors, these "parameteric matrix models" or
| PMMs outperform:
|
| * commonly used (zero- or low-parameter) regression models like
| XGBoost, random forests, kNN, and support vector machines on a
| variety of regression tasks, and
|
| * DNNs with 10x to 100x more parameters on small-scale image
| classification tasks like MNIST variants, CIFAR-10, and CIFAR-100
| -- albeit with a lot of feature engineering.
|
| It looks promising, but I cannot find a link to the authors' code
| for replicating their experiments.
| mnky9800n wrote:
| Morten was my PhD adviser. I'll ask him what's up.
| pdcook wrote:
| All of the code and data will be released with the peer-
| reviewed published version. If I remember, I'll come back to
| this thread and post the link.
| cs702 wrote:
| Thank you for taking the time to update everyone on HN.
|
| I've added your work to my reading list.
| pdcook wrote:
| Of course - I'm glad to see people are interested. I look
| forward to any feedback on our work either public or
| private.
| bionhoward wrote:
| Looks like a cool idea and it could benefit from a more complete
| and detailed illustration and explanation of the architecture.
|
| Reads like the authors skip to implications before clarifying the
| design.
|
| Also, a stylistic sidenote, narrower columns of text are much
| easier to read, newspapers and journals do this for good reason
| pdcook wrote:
| First author here. I agree with your points, we were
| constrained by the format and (especially) writing style
| expected by the journal we're submitting to. The Methods
| sections contain more explicit explanations as well as other
| analyses.
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