Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
Authors: Wonjae Kim, Yoonho Lee
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments reveal that applying DAFT yields similar performance to the original model while using fewer reasoning steps, showing that it implicitly learns to skip unnecessary steps. We conducted our experiments on the CLEVR3 [Johnson et al., 2017a] and GQA4 [Hudson and Manning, 2019] datasets. |
| Researcher Affiliation | Industry | Wonjae Kim Kakao Corporation Pangyo, Republic of Korea dandelin.kim@kakaocorp.com Yoonho Lee Kakao Corporation Pangyo, Republic of Korea eddy.l@kakaocorp.com |
| Pseudocode | Yes | Algorithm 1 Memory Update Procedure of MAC; Algorithm 2 Memory Update Procedure of DAFT MAC |
| Open Source Code | Yes | Our code is available at https://github.com/kakao/DAFT. |
| Open Datasets | Yes | We conducted our experiments on the CLEVR3 [Johnson et al., 2017a] and GQA4 [Hudson and Manning, 2019] datasets. |
| Dataset Splits | Yes | CLEVR has 700K questions for training and 150K questions for validation and test split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU models, CPU types, memory) for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) in the provided text. |
| Experiment Setup | No | The paper states: 'For a fair comparison, we used the same hyperparameters as the original MAC network [Hudson and Manning, 2018] and closely followed their experimental setup. [...] We list implementation details in Appendix B.' However, it defers the specific details to another paper and an appendix not provided in the main text, thus not explicitly listing concrete hyperparameter values or training configurations within the main body. |