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..
SNODE: Spectral Discretization of Neural ODEs for System Identification
Authors: Alessio Quaglino, Marco Gallieri, Jonathan Masci, Jan Koutník
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique (Chen et al., 2018), on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function. |
| Researcher Affiliation | Industry | NNAISENSE, Lugano, Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1 δ-SNODE training... Algorithm 2 α-SNODE training |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments (e.g., vehicle dynamics, multi-agent simulation) but does not provide access information or refer to a publicly available dataset for training. |
| Dataset Splits | No | The paper refers to 'cross-validation' conceptually but does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) for the experiments conducted. |
| Hardware Specification | Yes | Experiments were performed on a i9 Apple laptop with 32GB of RAM. |
| Software Dependencies | No | The paper mentions software like ADAM, SGD, and PyTorch for automatic differentiation, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Time horizon T = 10s and batch size of 100 were used. Learning rates were set to 10 2 for ADAM (for all methods) and 10 3 for SGD (for α-SNODE). For the α-SNODE method, γ = 3 and 10 iterations were used for the SGD and ADAM algorithms at each epoch, as outlined in Algorithm 2. |