Maximum-and-Concatenation Networks
Authors: Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu, Zhouchen Lin
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the commonly used CIFAR10 dataset, with the purpose of validating our theorems as well as the effectiveness of MCN. ... Figure 2 shows the training loss and testing accuracy with different number of layers. ... Table 1 shows the comparison results among all the three groups, in terms of both training loss and testing accuracy. |
| Researcher Affiliation | Collaboration | 1Key Lab. of Machine Perception (Mo E), School of EECS, Peking University 2School of Computer Science and Technology, Shandong University 3Sense Time Research 4B-DAT and CICAEET, School of Automation, Nanjing University of Information Science and Technology. |
| Pseudocode | No | The paper describes the MCN model using mathematical equations and textual descriptions, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code openly available or provide a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on the commonly used CIFAR10 dataset |
| Dataset Splits | No | The paper mentions using CIFAR-10 and CIFAR-100 datasets for training and testing, but it does not explicitly provide specific training, validation, and test split percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'batch normalization and Re LU' but does not specify any software names with version numbers or other reproducible software dependencies. |
| Experiment Setup | No | The paper states 'For detailed experimental settings and model configurations, please refer to the supplementary material.', indicating that specific details such as hyperparameter values are not provided in the main text. |