Copula-Nested Spectral Kernel Network
Authors: Jinyue Tian, Hui Xue, Yanfang Xue, Pengfei Fang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through rigorous theoretical analysis and experimental verification, Coke Net demonstrates superior performance and significant advancements over SOTA algorithms in the field.In this section, systematical experiments are performed to evaluate our proposed Coke Net. We first empirically demonstrate the efficacy of Coke Net in depicting relations between variables in the synthetic data. Then, we evaluate the performance of Coke Net compared with several state-of-the-art algorithms on six real-world datasets. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China 2Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We systematically evaluate the performance of Coke Net on six standard classification tasks from UCI repository (Blake, 1998).Blake, C. Uci repository of machine learning databases. http://www. ics. uci. edu/ mlearn/MLRepository. html, 1998. |
| Dataset Splits | No | The paper mentions training and testing data but does not explicitly provide details about a validation dataset split (e.g., percentages, counts, or methodology for its creation). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al., 2019)" and "ADAM (Kingma & Ba, 2015)" but does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | The scale of all networks is uniformly set to nfeature 512 256 nclasses. The architecture of the neural network, with Re LU functions as its activation function in Coke Net-R, is set to be as same as the copula net. Detailed information about the setting in each dataset is in the Appendix. All method is trained by ADAM (Kingma & Ba, 2015) using mean squared error (MSE) loss. The learning rate is 0.001 without weight decay. Accuracy is the measurement. |