Bayesian Semi-supervised Learning with Graph Gaussian Processes
Authors: Yin Cheng Ng, Nicolò Colombo, Ricardo Silva
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural networks in active learning experiments where labels are scarce. |
| Researcher Affiliation | Academia | Yin Cheng Ng1, Nicolò Colombo1, Ricardo Silva1,2 1Statistical Science, University College London 2The Alan Turing Institute {y.ng.12, nicolo.colombo, ricardo.silva}@ucl.ac.uk |
| Pseudocode | No | The paper describes the proposed model and its components in detail but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | Implementing the GGP with its variational inference algorithm amounts to implementing a new kernel function that follows Equation 12 in the GPflow Python package.1 1https://github.com/markvdw/GPflow-inter-domain. The link points to a general GPflow extension project, not specific open-source code for the GGP model described in the paper. |
| Open Datasets | Yes | The three benchmark data sets, as described in Table 2, are citation networks with bag-of-words (BOW) features, and the prediction targets are the topics of the scientific papers in the citation networks. |
| Dataset Splits | Yes | The semi-supervised classification experiments in this section exactly replicate the experimental setup in [25], where the GCN is known to perform well. (This references [25] which specifies validation splits). Furthermore, the model does not require a validation data set for early stopping to control over-fitting. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments, such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions 'GPflow Python package' and 'ADAM optimizer' but does not provide specific version numbers for these or any other software dependencies, such as Python version or library versions. |
| Experiment Setup | Yes | The GGP base kernel of choice is the 3rd degree polynomial kernel... We re-weighed the BOW features using the popular term frequency-inverse document frequency (TFIDF) technique [40]. The variational parameters and the hyper-parameters were jointly optimized using the ADAM optimizer [24]. |