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 [1].
Generative Time-series Modeling with Fourier Flows
Authors: Ahmed Alaa, Alex James Chan, Mihaela van der Schaar
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that Fourier flows perform competitively compared to state-of-the-art baselines. |
| Researcher Affiliation | Academia | Ahmed M. Alaa University of California, Los Angeles, USA EMAIL Alex J. Chan University of Cambridge, UK EMAIL Mihaela van der Schaar University of Cambridge, UK University of California, Los Angeles, USA Cambridge Center for AI in Medicine, UK The Alan Turing Institute, UK EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We conduct experiments on: Google stocks data, UCI Energy data set, and a longitudinal follow-up clinical data set for lung cancer patients. |
| Dataset Splits | No | The paper states using 1,000 synthetic time-series 'to train all baselines' but does not specify explicit train/validation/test splits. For real data, it mentions replicating the experimental setup from Yoon et al. (2019) but does not provide the specific split percentages or methodology within this paper. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions machine learning models (e.g., Bi RNN) but does not specify version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | We train all models with 1,000 iterations and a batch size of 128 we then generate 1,000 synthetic time-series from each trained model. ... a Fourier flow (FF) model with a composition of 10 flows and a (single-layer) Bi RNN with 200 hidden units |