Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Authors: Stéphanie ALLASSONNIERE, Juliette Chevallier, Stephane Oudard
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data validate this choice. The model is then applied to the metastatic renal cancer chemotherapy monitoring: we run estimations on RECIST scores of treated patients and estimate the time they escape from the treatment. Experiments highlight the role of the different parameters on the response to treatment. |
| Researcher Affiliation | Academia | Juliette Chevallier CMAP, École polytechnique juliette.chevallier@polytechnique.edu Pr Stéphane Oudard Oncology Department USPC, AP-HP, HEGP Stéphanie Allassonnière CRC, Université Paris Descartes stephanie.allassonniere@parisdescartes.fr |
| Pseudocode | No | The paper refers to supplementary material for details about algorithmics, but does not include any pseudocode or algorithm blocks in the main text. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | We now run our estimation algorithm on real data from HEGP. We have performed the estimation over a drove of 176 patients of the HEGP. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU, CPU models, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of 'MONOLIX MOdèles NOn LInéaires à effets mi Xtes' but does not provide a specific version number or other software dependencies with versions. |
| Experiment Setup | Yes | Moreover, to put the algorithm on a more realistic situation, the synthetic individual times are non-periodically spaced, individual sizes vary between 12 and 18 and the observed values are noisy (σ = 3). We present here a run with a low residual standard variation in respect to the amplitude of the trajectories and complexity of the dataset: σ = 14.50 versus max(γinit 0 , γfin 0 ) γescap 0 = 452.4. We have run the algorithm several times, with different proposal laws for the sampler (a Symmetric Random Walk Hasting-Metropolis within Gibbs one) and different priors. |