Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Nonlinear Pairwise Layer and Its Training for Kernel Learning
Authors: Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Li Li
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we find that the proposed structure outperforms other state-of-the-art kernel-based algorithms on various benchmark datasets, and thus the effectiveness of the incorporated pairwise layer with its training approach is demonstrated. |
| Researcher Affiliation | Academia | Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University School of Computer Science and Engineering, Nanjing University of Science and Technology Department of Automation, Tsinghua University |
| Pseudocode | Yes | Algorithm 1: Optimization for (15) via ADMM; Algorithm 2: Algorithm for the KNPL model. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | In the experiments, fifteen real-word datasets from UCI Machine Learning Repository (Blake and Merz 1998) are used to evaluate the performance of KNPL with other kernel learning algorithms. |
| Dataset Splits | Yes | In our model, the regularization parameter γ is tuned by 5-fold cross validation. That is, we randomly partition the training data into 5 subsets, one of which is used for validation in turn and the remaining ones for training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions using methods like "SMO algorithm" and "Alternating Direction Method of Multipliers (ADMM)", but it does not list any specific software components with version numbers. |
| Experiment Setup | Yes | In our model, the regularization parameter γ is tuned by 5-fold cross validation... Stopping criteria kmax = 15 and ϵ = 10 4. ... Set the maximum iteration number T = 10. ... where ρ = 1.1 is the parameter that makes β gradually increase in each loop. |