An online passive-aggressive algorithm for difference-of-squares classification
Authors: Lawrence Saul
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental results We experimented on the INFIMNIST data set of handwritten digit images [7], a purposefully constructed superset of the original MNIST data set [73]. |
| Researcher Affiliation | Academia | Lawrence K. Saul Department of Computer Science and Engineering University of California, San Diego 9500 Gilman Drive, Mail Code 0404 La Jolla, CA 92093-0404 saul@cs.ucsd.edu |
| Pseudocode | No | The paper describes algorithms using mathematical equations and text, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not state that source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We experimented on the INFIMNIST data set of handwritten digit images [7], a purposefully constructed superset of the original MNIST data set [73]. |
| Dataset Splits | No | The paper mentions a test set and training examples but does not explicitly state a validation dataset split or its size/percentage. |
| Hardware Specification | Yes | In total we purchased several hundred CPU-hours on an externally managed cluster of Intel Xeon Gold 6132 processors. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for its experiments. |
| Experiment Setup | Yes | We initialized the parameters of the linear model with zero values and those of the Do S models with small random values. Specifically, we sampled the elements of U and V from a zero-mean normal distribution whose variance was inversely proportional to the number of elements in these matrices. |