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..
Shape analysis for time series
Authors: Thibaut Germain, Samuel Gruffaz, Charles Truong, Alain Durmus, Laurent Oudre
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase the advantages of our representation compared to existing methods using synthetic data and real-world examples motivated by biomedical applications. |
| Researcher Affiliation | Academia | Thibaut Germain1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Samuel Gruffaz1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Charles Truong1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Laurent Oudre1 Centre Borelli, ENS Paris-Saclay 4 av. des sciences, 91190 Alain Durmus CMAP, CNRS, Ecole polytechnique Institut Polytechnique de Paris 91120 Palaiseau, France |
| Pseudocode | No | The paper describes algorithms and equations but does not provide a formal pseudocode block or algorithm environment labeled as such. |
| Open Source Code | Yes | The source code is available on Github4. https://github.com/thibaut-germain/TSLDDMM |
| Open Datasets | Yes | We selected 15 shape-based datasets (7 univariates and 8 multivariates) from the from the University of East Anglia (UEA) and the University of California Riverside (UCR) Time Series Classification Repository8 [15, 3]. |
| Dataset Splits | Yes | Spilt the dataset in train 75%, validation 15%, and test 15%. |
| Hardware Specification | Yes | All experiments were performed on a Debian 6.1.69-1 server with NVIDIA RTX A2000 12GB GPU, Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz, and 250 GB of RAM. |
| Software Dependencies | No | The paper mentions specific libraries like "JAX library" and "OPTAX library" but does not include specific version numbers for these or other key software components, which is required for reproducibility. |
| Experiment Setup | Yes | The optimization hyperparameter details are given in Appendix E.1. By default, we set nb_steps to 400 and ηM to 0.1. To learn TS-LDDMM (resp. LDDMM) representations, the velocity field kernel KG is set to (c0, c1, σT,0, σT,1, σx) = (1, 0.1, 0.33 l, 1, nd), (resp. (σT , σx) = (0.33 l, nd)) where l is the average time series length and nd the number of dimensions. |