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..
Symplectic Neural Gaussian Processes for Meta-learning Hamiltonian Dynamics
Authors: Tomoharu Iwata, Yusuke Tanaka
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate that the proposed method outperforms existing methods for predicting dynamics from a small number of observations in target systems. |
| Researcher Affiliation | Industry | Tomoharu Iwata , Yusuke Tanaka NTT Corporation EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta-learning procedure of our SNGP model. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of its source code. |
| Open Datasets | No | The paper describes generating data from six types of dynamical systems (mass-spring, pendulum, Duffing with and without friction) with randomly determined physical parameters and initial conditions, rather than using a pre-existing publicly available dataset with a specific link or citation. |
| Dataset Splits | Yes | For each type, five systems were used for meta-training, three for metavalidation, and six for meta-test. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'functorch' but does not specify their version numbers. |
| Experiment Setup | Yes | For obtaining system representation in Eq. (3), we used the bidirectional LSTM [Graves and Graves, 2012] for RNN with 32 hidden units, where the sequence of the states was used for input. For NNz and NNk, we used three-layered feedforward neural networks with 32 hidden and output units. For NNm, we used four-layered feed-forward neural networks with 32 hidden units. For the activation function, we used the hyperbolic tangent. We optimized our models using Adam [Kingma and Ba, 2015] with learning rate 10^-3, and batch dataset size four. The meta-validation datasets were used for early stopping, for which the maximum number of meta-training epochs was 5,000. |