Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning interpretable continuous-time models of latent stochastic dynamical systems
Authors: Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,798 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,014 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach on simulated data from different nonlinear dynamical systems. (Abstract) and In this section, we apply our algorithm to data generated from different nonlinear dynamical systems. (Section 5, Experiments) |
| Researcher Affiliation | Academia | 1Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom 2Stanford University, Palo Alto, California, USA. |
| Pseudocode | No | The paper describes its algorithms and equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper uses simulated data from known dynamical systems, but does not provide concrete access information (link, DOI, repository, or citation) for the specific simulated datasets used in their experiments. |
| Dataset Splits | No | The paper describes how data was simulated (e.g., '20 trials'), but does not specify explicit training, validation, or test dataset splits in terms of percentages or counts for a defined dataset. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'forward Euler method' for solving ODEs but does not specify any software names with version numbers for its implementation or other dependencies. |
| Experiment Setup | Yes | In all experiments, we choose an exponentiated quadratic covariance function in the prior over the dynamics f and initialise the inducing point means and Jacobian matrices at zero. Each fixed point observation s uncertainty is initialised with a standard deviation of 0.1. We generate C and d by drawing their entries from Gaussian distributions unless otherwise stated, and initialise our algorithm at these parameter values. For inference, we solve the ODEs (19)-(22) using the forward Euler method with t = 1ms. Unless stated otherwise, the link function is the indentity g(z) = z. |