Neural Relational Inference with Fast Modular Meta-learning
Authors: Ferran Alet, Erica Weng, Tomás Lozano-Pérez, Leslie Pack Kaelbling
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement our solution in Py Torch (Paszke et al., 2017), using the Adam optimizer (Kingma & Ba, 2014); details and pseudo-code can be found in the appendix and code can be found at https://github.com/FerranAlet/modular-metalearning. We follow the choices of Kipf et al. (2018) whenever possible to make results comparable. Please see the arxiv version for complete results. We begin by addressing two problems on which NRI was originally demonstrated, then show that our approach can be applied to the novel problem of inferring the existence of unobserved nodes. 5.1 Predicting physical systems Two datasets from Kipf et al. (2018) are available online (https://github.com/ethanfetaya/NRI/); in each one, we observe the state of dynamical system for 50 time steps and are asked both to infer the relations between object pairs and to predict their states for the next 10 time steps. |
| Researcher Affiliation | Academia | Ferran Alet, Erica Weng, Tomás Lozano Pérez, Leslie Pack Kaelbling MIT Computer Science and Artificial Intelligence Laboratory {alet,ericaw,tlp,lpk}@mit.edu |
| Pseudocode | Yes | details and pseudo-code can be found in the appendix |
| Open Source Code | Yes | code can be found at https://github.com/FerranAlet/modular-metalearning |
| Open Datasets | Yes | Two datasets from Kipf et al. (2018) are available online (https://github.com/ethanfetaya/NRI/); in each one, we observe the state of dynamical system for 50 time steps and are asked both to infer the relations between object pairs and to predict their states for the next 10 time steps. |
| Dataset Splits | No | No specific training/validation/test dataset splits (e.g., 80/10/10 split or sample counts) are explicitly stated in the provided text. The paper mentions 'train split' and 'test split' in the context of the meta-learning algorithm's inner loop, but not for the overall dataset partitioning used for evaluating the model. |
| Hardware Specification | No | No specific hardware (e.g., GPU models, CPU types, or cloud compute instances) used for experiments is mentioned in the paper. |
| Software Dependencies | No | We implement our solution in Py Torch (Paszke et al., 2017), using the Adam optimizer (Kingma & Ba, 2014); No specific version numbers for PyTorch or Adam are provided. |
| Experiment Setup | No | The paper states 'details and pseudo-code can be found in the appendix', suggesting that experimental setup details like hyperparameters might be there, but they are not explicitly provided in the main text. |