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..
Pointer Graph Networks
Authors: Petar Veličković, Lars Buesing, Matthew Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results, summarised in Table 1, clearly indicate outperformance and generalisation of our PGN model, especially on the larger-scale test sets. and Experimental setup As in [47, 55], we evaluate out-of-distribution generalisation training on operation sequences for small input sets (n = 20 entities with ops = 30 operations), then testing on up to 5 larger inputs (n = 50, ops = 75 and n = 100, ops = 150). |
| Researcher Affiliation | Industry | Petar Veliˇckovi c, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals and Charles Blundell Deep Mind EMAIL |
| Pseudocode | Yes | Figure 2: Pseudocode of DSU operations; initialisation and find(u) (Left), union(u, v) (Middle) and query-union(u, v), giving ground-truth values of ˆy(t) (Right). |
| Open Source Code | Yes | for brevity, we delegate further descriptions of their operations to Appendix C, and provide our C++ implementation of the LCT in the supplementary material. |
| Open Datasets | No | The paper describes generating operations by sampling input node pairs uniformly at random, which forms a custom dataset. No concrete access information (specific link, DOI, repository, or formal citation with authors/year) for a publicly available or open dataset was provided. |
| Dataset Splits | Yes | Experimental setup As in [47, 55], we evaluate out-of-distribution generalisation training on operation sequences for small input sets (n = 20 entities with ops = 30 operations), then testing on up to 5 larger inputs (n = 50, ops = 75 and n = 100, ops = 150). In line with [47], we generate 70 sequences for training, and 35 sequences across each test size category for testing. We perform early stopping, retrieving the model which achieved the best query F1 score on a validation set of 35 small sequences (n = 20, ops = 30). |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were provided. |
| Software Dependencies | No | The paper mentions JAX [2] and Haiku [18] but does not provide specific version numbers for these or any other software components. |
| Experiment Setup | Yes | All models compute k = 32 latent features in each layer, and are trained for 5, 000 epochs using Adam [22] with learning rate of 0.005. We perform early stopping, retrieving the model which achieved the best query F1 score on a validation set of 35 small sequences (n = 20, ops = 30). |