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 [1].
Memory-Gated Recurrent Networks
Authors: Yaquan Zhang, Qi Wu, Nanbo Peng, Min Dai, Jing Zhang, Hu Wang10956-10963
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a combination of comprehensive simulation studies and empirical experiments on a range of public datasets, we show that our proposed m GRN architecture consistently outperforms state-of-the-art architectures targeting multivariate time series. |
| Researcher Affiliation | Collaboration | Yaquan Zhang,3 Qi Wu,2* Nanbo Peng,1 Min Dai,4 Jing Zhang,2 Hu Wang1 1 JD Digits 2 City University of Hong Kong 3 National University of Singapore, Department of Mathematics and Risk Management Institute 4National University of Singapore, Department of Mathematics, Risk Management Institute, and Chong-Qing & Suzhou Research Institutes |
| Pseudocode | No | The paper uses mathematical equations to describe the model but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/yaquanzhang/m GRN |
| Open Datasets | Yes | All data sets are publicly available. All results, except for those of m GRN, are taken from the two papers... MIMIC-III data set (Johnson et al. 2016)... UEA multivariate time series data sets (Bagnall et al. 2018). |
| Dataset Splits | Yes | The ๏ฌrst 70, 000 observations are used to train models. The next 15, 000 observations are used for validation. The ๏ฌnal performance is evaluated in the last 15, 000 observations. |
| Hardware Specification | Yes | Experiments are coded with Pytorch (Paszke et al. 2019) and performed on NVIDIA TITAN Xp GPUs with 12 GB memory. |
| Software Dependencies | No | Experiments are coded with Pytorch (Paszke et al. 2019), but no specific version number for Pytorch or any other software dependency is provided. |
| Experiment Setup | Yes | To make predictions, we include observations of the past 5 steps in neural networks... we limit the total number of trainable parameters to be around 1.8 thousand for all models... We also tune the learning rates via grid searches within {10 4, 5 10 4, 10 3}. and perform grid searches on hyperparameters such as variable grouping, dimensions of the marginal and joint components ( N and N), learning rates, and dropouts. |