Towards Bridging Sample Complexity and Model Capacity
Authors: Shibin Mei, Chenglong Zhao, Shengchao Yuan, Bingbing Ni1972-1980
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct extensive experiments to evaluate the sample complexity and model capacity of classification tasks on various datasets, which demonstrate the validity of the proposed analysis method. Our experiments are based on Py Torch. |
| Researcher Affiliation | Academia | Shibin Mei, Chenglong Zhao, Shengchao Yuan, Bingbing Ni* Shanghai Jiao Tong University, Shanghai 200240, China {adair327, cl-zhao, sc yuan, nibingbing}@sjtu.edu.cn |
| Pseudocode | No | The paper does not contain STRUCTURED PSEUDOCODE OR ALGORITHM BLOCKS (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide CONCRETE ACCESS TO SOURCE CODE (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We conduct our experiments mainly on three datasets, including MNIST, CIFAR-10 and SVHN. |
| Dataset Splits | No | The paper mentions "train set" and "test set" but does not provide SPECIFIC DATASET SPLIT INFORMATION (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide SPECIFIC HARDWARE DETAILS (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not provide SPECIFIC ANCILLARY SOFTWARE DETAILS (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper states "Hyper-parameters about classifiers can be found in the supplementary materials." and "The details of model training can be found in the supplementary materials.", indicating that specific experimental setup details are not provided in the main text. |