How Modular should Neural Module Networks Be for Systematic Generalization?
Authors: Vanessa D'Amario, Tomotake Sasaki, Xavier Boix
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a series of experiments on three VQA datasets (VQA-MNIST, SQOOP, and CLEVRCo Gen T), our results reveal that tuning the degree of modularity, especially at the image encoder stage, reaches substantially higher systematic generalization. |
| Researcher Affiliation | Collaboration | Vanessa D Amario 1, 3 Tomotake Sasaki 2, 3 Xavier Boix 1, 3 1 Massachusetts Institute of Technology, USA 2 Fujitsu Limited, Japan 3 Center for Brains, Minds and Machines, USA |
| Pseudocode | No | The paper describes module definitions with mathematical formulas but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data can be found in the following github repository: https://github.com/vanessadamario/understanding_reasoning.git. |
| Open Datasets | Yes | Code and data can be found in the following github repository: https://github.com/vanessadamario/understanding_reasoning.git. |
| Dataset Splits | Yes | We perform a grid-search over the NMNs hyper-parameters, and evaluate the accuracy using an in-distribution validation split at the last iteration of the training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For each experiment, we tuned the hyper-parameters by performing a random-search over 100 sets of hyper-parameters. We used the Adam optimizer [23] with learning rates equal to {10e-3, 10e-4, 10e-5, 10e-6}, and dropout rates equal to {0, 0.2, 0.4, 0.6, 0.8}. |