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| Date | Stars |
|---|---|
| 2026-07-31 | 276 |
| 2026-08-03 | 276 |
| 2026-08-04 | 276 |
| 2026-08-06 | 276 |
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# Gated Graph Sequence Neural Networks This is the code for our ICLR'16 paper: * Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel. [*Gated Graph Sequence Neural Networks*](http://arxiv.org/abs/1511.05493). International Conference on Learning Representations, 2016. Please cite the above paper if you use our code. The code is released under the [MIT license](LICENSE). ### Testing Run `th test.lua` to test all the modules in the ggnn and rnn libraries. ### Reproducing the bAbI tasks and graph algorithms experiment results To run the bAbI experiments, and experiments on the two extra sequence tasks: 1. Go into `babi/data`, run `bash get_10_fold_data.sh` to get 10 folds of bAbI data for 5 tasks (4, 15, 16, 18, 19) and do some preprocessing. 2. Go into `babi/data/extra_seq_tasks`, run `bash generate_10_fold_data.sh` to get 10 folds of data for the two extra sequence tasks. 3. Go back to `babi/` and use `run_experiments.py` to run the GGNN/GGS-NN experiments, e.g. `python run_experiments.py babi18` runs GGNN on bAbI task 18 for all 10 folds of data. 4. Use `run_rnn_baselines.py` to run RNN/LSTM baseline experiments, e.g. `python run_rnn_baselines.py babi18 lstm` runs LSTM on bAbI task 18 for all 10 folds of data. ### Notes * Make sure `"./?.lua"` and `"./?/init.lua"` are on your lua path. For example by `export LUA_PATH="./?.lua;./?/init.lua;$LUA_PATH"`. * The experiment results may differ slightly from what we reported in the paper, as the datasets are randomly generated and will be different from run to run.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c8b9dc070ce6a85a, llm:Repository description: 'Gated Graph Sequence Neural Networks' (GGNN). Language: Lua. No topics provided.
matched fp:c8b9dc070ce6a85a, llm:Repository description: 'Gated Graph Sequence Neural Networks' (GGNN). Language: Lua. No topics provided.
matched fp:c8b9dc070ce6a85a, llm:Repository description: 'Gated Graph Sequence Neural Networks' (GGNN). Language: Lua. No topics provided.