Neural Production Systems
Authors: Anirudh Goyal ALIAS PARTH GOYAL, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael C. Mozer, Yoshua Bengio
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a series of experiments, we demonstrate that this architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information. This disentangling of knowledge achieves robust future-state prediction in rich visual environments, outperforming state-of-the-art methods using GNNs, and allows for the extrapolation from simple (few object) environments to more complex environments. |
| Researcher Affiliation | Collaboration | 1 Mila, University of Montreal, 2 Google Deepmind, 3 Waverly, 4 Google Research, Brain Team. |
| Pseudocode | No | The paper mentions 'detailed algorithms for the sequential and parallel regimes in Appendix' but does not show them in the provided text. No explicit 'Pseudocode' or 'Algorithm' block is visible in the provided content. |
| Open Source Code | Yes | We include the code for reproducing our experiments |
| Open Datasets | Yes | We validate our model on a colored version of this dataset. This is a next-step prediction task in which the model is tasked with predicting the final binary mask of each ball. We compare the following methods: (a) SCOFF (Goyal et al., 2020) |
| Dataset Splits | Yes | We consider two evaluation settings. (1) Test setting: The number of rollout timesteps is same as that seen during training (i.e. t = 15); (2) Transfer Setting: The number of rollout timesteps is higher than that seen during training (i.e. t = 30). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., specific GPU/CPU models, processor types, or memory details) used for running its experiments. It only mentions 'Google cloud credits' which is not specific enough. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Here we briefly outline the tasks considered and direct the reader to the Appendix for full details on each task and details on hyperparameter settings. |