3 ms·
Huh, interesting. According to the authors, these "parameteric matrix models" or PMMs outperform: * commonly used (zero- or low-parameter) regression models l
by cs702 2y ago
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.