Stability-Informed Initialization of Neural Ordinary Differential Equations
Authors: Theodor Westny, Arman Mohammadi, Daniel Jung, Erik Frisk
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the initialization method is displayed across several learning benchmarks and industrial applications. To assess the performance and usability of the proposed technique, several experiments are conducted across a diverse set of problem domains. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Link oping University, Link oping, Sweden. |
| Pseudocode | Yes | Algorithm 1 Stability Region Rejection Sampling |
| Open Source Code | Yes | Implementations are made publicly available.2 https://github.com/westny/neural-stability |
| Open Datasets | Yes | the original MNIST (Le Cun et al., 1998) and CIFAR10 (Krizhevsky et al., 2009) datasets are modified for a sequential classification task. The second dataset, referred to as the Human Activity dataset (Vidulin et al., 2010), features data from five individuals, each wearing four localization tags (left ankle, right ankle, belt, chest). The third dataset, referenced as the Air Quality dataset (Vito, 2016) encompasses a set of air quality measurements from an urban monitoring station in Italy. |
| Dataset Splits | No | The paper describes training and testing phases for different experiments but does not explicitly provide specific percentages or counts for training, validation, and test dataset splits. |
| Hardware Specification | No | Computations were enabled by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre. |
| Software Dependencies | No | No specific software versions (e.g., Python 3.x, PyTorch 1.x) or versions of other key libraries/solvers were explicitly listed. |
| Experiment Setup | No | The paper mentions training with 'different hyperparameters' and that 'batch size and learning rate may differ with the task but is always the same for all methods', but it does not provide concrete values for these hyperparameters or other training settings like epochs or optimizer details in the main text. |