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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Latent Convergent Cross Mapping
Authors: Edward De Brouwer, Adam Arany, Jaak Simm, Yves Moreau
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our approach on data sets from physical and neurophysiology models, namely a double pendulum and neurons activity data. We show that our method detects the right causal topology in all cases, outperforming multi-spatial CCM, as well as baselines designed to address the sporadicity of the time series. The code is available at https: //github.com/edebrouwer/latent CCM. |
| Researcher Affiliation | Academia | Edward De Brouwer ESAT-STADIUS KU LEUVEN Leuven, 3001, Belgium EMAIL Adam Arany ESAT-STADIUS KU LEUVEN Leuven, 3001, Belgium EMAIL Jaak Simm ESAT-STADIUS KU LEUVEN Leuven, 3001, Belgium EMAIL Yves Moreau ESAT-STADIUS KU LEUVEN Leuven, 3001, Belgium EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https: //github.com/edebrouwer/latent CCM. |
| Open Datasets | No | The paper describes generating its own datasets (double pendulum, neural activity data) for evaluation but does not provide concrete access information (link, DOI, repository) for these specific datasets to be publicly available. |
| Dataset Splits | Yes | We used 80% of available windows for training and used the remaining 20% for hyperparameter tuning with the MSE prediction on future samples used as model selection criterion. |
| Hardware Specification | No | The acknowledgements section states: 'We also thank Nvidia for supporting this research by donating GPUs.' However, this does not provide specific details about the GPU models, CPU models, or other hardware specifications used for running the experiments. |
| Software Dependencies | Yes | We generate time series of the average membrane potential of two populations of leaky integrate-and-๏ฌre neurons with alpha-function shaped synaptic currents (iaf psc alpha) simulated by NEST-2.20.0 (Fardet et al., 2020). [...] Implementation was done with GP๏ฌow (Matthews et al., 2017). |
| Experiment Setup | Yes | We used 80% of available windows for training and used the remaining 20% for hyperparameter tuning with the MSE prediction on future samples used as model selection criterion. [...] To account for the short length of time series usually encountered in the real world, we randomly split the trajectories in windows of 10 seconds. To simulate sporadicity, we sample observation uniformly at random with an average rate of 4 samples per second. Furthermore, for each of those samples, we apply an observation mask that keeps each individual dimension with probability 0.3. |