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..
How Modular should Neural Module Networks Be for Systematic Generalization?
Authors: Vanessa D'Amario, Tomotake Sasaki, Xavier Boix
NeurIPS 2021 | Venue PDF | 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}. |