Neural Execution of Graph Algorithms
Authors: Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 EXPERIMENTAL SETUP |
| Researcher Affiliation | Collaboration | Petar Veliˇckovi c Deep Mind petarv@google.com Rex Ying Stanford University rexying@stanford.edu Matilde Padovano University of Cambridge mp861@cam.ac.uk Raia Hadsell Deep Mind raia@google.com Charles Blundell Deep Mind cblundell@google.com |
| Pseudocode | No | The paper describes algorithms using mathematical equations (e.g., Equation 5-10) and natural language, but it does not include explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the availability of source code for the described methodology, nor does it provide links to a code repository. |
| Open Datasets | No | The paper states, 'To provide our learner with a wide variety of input graph structure types, we follow prior work (You et al., 2018; 2019) and generate undirected graphs from seven categories.' It describes how graphs were generated but does not provide access information for a publicly available dataset. |
| Dataset Splits | Yes | For each category, we generate 100 training and 5 validation graphs of only 20 nodes. For testing, 5 additional graphs of 20, 50 and 100 nodes are generated per category. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions the use of 'Adam SGD optimiser (Kingma & Ba, 2014)' but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In all cases, the neural networks compute a latent dimension of K = 32 features, and are optimised using the Adam SGD optimiser (Kingma & Ba, 2014) on the binary cross-entropy for the reachability predictions, mean squared error for the distance predictions, categorical cross-entropy for the predecessor node predictions, and binary cross-entropy for predicting termination (all applied simultaneously). We use an initial learning rate of 0.0005, and perform early stopping on the validation accuracy for the predecessor node (with a patience of 10 epochs). |