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..
Learning Graphical State Transitions
Authors: Daniel D. Johnson
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS I evaluated the GGT-NN model on the b Ab I tasks, a set of simple natural-language tasks, where each task is structured as a sequence of sentences followed by a query (Weston et al., 2016). ... Results are shown in Tables 1 and 2. |
| Researcher Affiliation | Academia | Daniel D. Johnson Department of Computer Science Harvey Mudd College 301 Platt Boulevard EMAIL |
| Pseudocode | Yes | Algorithm 1 Graph Transformation Pseudocode |
| Open Source Code | Yes | 1The code for each transformation, and for the GGT-NN model itself, is available at https://github. com/hexahedria/gated-graph-transformer-network. |
| Open Datasets | Yes | I evaluated the GGT-NN model on the b Ab I tasks, a set of simple natural-language tasks, where each task is structured as a sequence of sentences followed by a query (Weston et al., 2016). ... The first task used was a 1-dimensional cellular automaton, specifically the binary cellular automaton known as Rule 30 (Wolfram, 2002). |
| Dataset Splits | No | No explicit detailed information on training/validation/test splits (e.g., percentages, absolute counts for validation set, or specific stratified methods) was provided. The paper states: 'I trained two versions of the GGT-NN model for each task: one with and one without direct reference. ... The GGT-NN model was trained on 1000 examples of the Rule 30 automaton... and 20,000 examples of Turing machines...' |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, number of cores) used for experiments were mentioned. The acknowledgments state: 'I would like to thank Harvey Mudd College for computing resources. I would also like to thank the developers of the Theano library, which I used to run my experiments. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575.' |
| Software Dependencies | No | No specific version numbers for software dependencies were provided. The acknowledgments mention: 'I would like to thank the developers of the Theano library, which I used to run my experiments.' |
| Experiment Setup | Yes | Depending on the configuration of the transformations, a GGT-NN can take textual or graph-structured input, and produce textual or graph-structured output. ... Depending on the task, direct reference updates and per-sentence propagation can be enabled or disabled. The output function foutput will depend on the specific type of answer desired. If the answer is a single word, foutput can be a multilayer perceptron followed by a softmax operation. If the answer is a sequence of words, foutput can use a recurrent network (such as a GRU) to produce a sequence of outputs. |