Dual Algorithmic Reasoning
Authors: Danilo Numeroso, Davide Bacciu, Petar Veličković
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess the benefits of the dual algorithmic reasoning approach, we test the learning model in two specific scenarios. First, we train and test the DAR pipeline on synthetic-generated graphs, to evaluate the benefits in the key task of algorithmic learning (section 4.1). Then, to evaluate the generality of the model we test it on a real-world graph learning task. |
| Researcher Affiliation | Collaboration | Danilo Numeroso University of Pisa danilo.numeroso@phd.unipi.it Davide Bacciu University of Pisa davide.bacciu@unipi.it Petar Veliˇckovi c Deep Mind petarv@deepmind.com |
| Pseudocode | Yes | A PSEUDO-CODE A.1 FORD-FULKERSON PSEUDO-CODE Algorithm 1 Ford-Fulkerson A.2 CORRECTIVE FLOW PROCEDURE PSEUDO-CODE Algorithm 2 Flow correction algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for their methodology or a direct link to a code repository. |
| Open Datasets | Yes | To generate train, validation and test sets we follow the standard CLRS benchmark (Veliˇckovi c et al., 2022) setup. Specifically, we sample 1000 2-community training graphs with 16 nodes each. |
| Dataset Splits | Yes | To generate train, validation and test sets we follow the standard CLRS benchmark (Veliˇckovi c et al., 2022) setup. Specifically, we sample 1000 2-community training graphs with 16 nodes each. The validation set is used to assess in-distribution performance, thus comprising 128 2-community graphs with still 16 nodes. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications used to run the experiments (e.g., specific GPU or CPU models, memory details). |
| Software Dependencies | No | The paper mentions optimizers like "SGD optimiser" and "Adam optimiser (Kingma & Ba, 2015)", but it does not specify versions for any programming languages, libraries, or other software components used for implementation. |
| Experiment Setup | Yes | We train all models for 20,000 epochs with the SGD optimiser and we average the results across 5 runs. We also use teacher forcing with a decaying factor of 0.999. To choose optimal hyperparameters, e.g. learning rate, hidden dimension, we employ a bi-level random search scheme, where the first level samples values of hyperparameters in a large range of values, while the second one refines the search based on the first level results. We choose the best hyperparameters based on the validation error on F . Aggregated validation loss curves are shown in Figure 2(a). For further details on the model selection, refer to the appendix. (Appendix B HYPERPARAMETER OPTIMISATION, Table 4, Table 5) |