Counterfactual Dynamics Forecasting – a New Setting of Quantitative Reasoning
Authors: Yanzhu Liu, Ying Sun, Joo-Hwee Lim
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on two dynamical systems demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Institute for Infocomm Research (I2R) & Centre for Frontier AI Research (CFAR), A*STAR, Singapore |
| Pseudocode | Yes | Algorithm 1: Counterfactual Dynamics Forecasting |
| Open Source Code | Yes | Code and data are available on https://github.com/yanzhuliu/cf lnn. |
| Open Datasets | Yes | Code and data are available on https://github.com/yanzhuliu/cf lnn. |
| Dataset Splits | Yes | For each dataset, 800, 100 and 100 pairs of trajectories are used as the training, validation and testing set respectively following (Zhong, Dey, and Chakraborty 2021). |
| Hardware Specification | No | The paper does not mention any specific hardware used for running the experiments, such as GPU models, CPU types, or cloud computing instances with their specifications. |
| Software Dependencies | No | The paper mentions software components like "odeint is the integration solver (Chen et al. 2018)" and states "All network backbones are implemented in the same way as (Zhong, Dey, and Chakraborty 2021)", but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper describes the loss function used (mean absolute error in Eq. 5) but does not provide specific hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings, nor other system-level training configurations in the main text. |