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..
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Authors: Louis-Pascal Xhonneux, Andreea-Ioana Deac, Petar Veličković, Jian Tang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To investigate this empirically we create a dataset including 9 algorithms and 3 different graph types. We validate this empirically and show how instead multi-task learning can be used to achieve the transfer of algorithmic reasoning knowledge. |
| Researcher Affiliation | Collaboration | Louis-Pascal A. C. Xhonneux Université de Montréal Mila EMAIL Andreea Deac Université de Montréal Mila EMAIL Petar Veliˇckovi c Deep Mind, London UK EMAIL Jian Tang HEC Montréal Mila EMAIL |
| Pseudocode | Yes | Algorithm 1 Parallel Input: graph G, weights w, source index i initialise_nodes(G.vertices, i) repeat for (u, v) G.edges() do relax_edge(u, v, w) end for until none of the nodes change |
| Open Source Code | No | All code to generate data and train models will be released upon acceptance with an MIT license. |
| Open Datasets | No | To investigate this empirically we create a dataset including 9 algorithms and 3 different graph types. |
| Dataset Splits | No | We train using ADAM [28] with a learning rate of 0.0005, a batch size of 64, and use early stopping with a patience of 10 to prevent overfitting. We test on graphs size 20, 50, and 100 nodes. |
| Hardware Specification | Yes | Each experiment was executed on a V100 GPU in less than 5 hours for the longest experiment. |
| Software Dependencies | No | The paper mentions using ADAM [28] as the optimizer but does not specify version numbers for any software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | For all experiments we use 5,000 graphs of each type (Erdos-Renyi (ER), Barabasi-Albert (BA), 2d-Grids (2d-G)) with 20 nodes each. We train using ADAM [28] with a learning rate of 0.0005, a batch size of 64, and use early stopping with a patience of 10 to prevent overfitting. We test on graphs size 20, 50, and 100 nodes. The hidden embedding size is set to 32 except for NE++ for multi-task experiments, where it is 16 to account for the additional expressivity of having several encoders. |