Fuzzy Learning Machine
Authors: Junbiao Cui, Jiye Liang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The systematic experimental results on a large number of data sets show that FLM can achieve excellent performance, even with the simple implementation. |
| Researcher Affiliation | Academia | 1 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China. |
| Pseudocode | Yes | Algorithm 1: The training process of FLM; Algorithm 2: The test process of FLM |
| Open Source Code | Yes | The code of the proposed method is provided in supplementary material. |
| Open Datasets | Yes | Experimental settings In this section, the MNIST data set [31] is chosen to demonstrate the working mechanism of NN-FLM. ... 121 benchmark data sets (see Table 2 in Appendix A.4.2). ... And the data sets can be download from the links in Appendix A.4. |
| Dataset Splits | No | The paper defines Dtrain and Dtest but does not explicitly mention or specify a validation dataset split. |
| Hardware Specification | No | The paper states 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]' and does not provide specific hardware details in the main text. |
| Software Dependencies | No | The paper mentions that code is provided in supplementary material but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In NN-FLM, a 5-layer convolutional neural network is used as the feature extraction network, and the fuzzy parameters are fixed as α = 0.2, β = 0.8. ... NN-FLM adopts a 3-layer fully connected network as the feature extraction network. The fuzzy parameters are fixed as α = 0.2, β = 0.8. |