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..
Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference
Authors: Khalid Oublal, Said Ladjal, David Benhaiem, Emmanuel LE BORGNE, François Roueff
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method qualitatively and quantitatively across various datasets with ground-truth labels, examining the generalization capabilities of the learned representations on correlated data. |
| Researcher Affiliation | Collaboration | Institute Polytechnique de Paris, Telecom Paris LTCI/S2A, One Tech Total Energies, DS&AI |
| Pseudocode | Yes | D.8 PSEUDOCODE DIOSC Cosine Similarity |
| Open Source Code | Yes | Code available at https://institut-polytechnique-de-paris.github. io/time-disentanglement-lib. |
| Open Datasets | Yes | Datasets. We conducted experiments on three public datasets: UK-DALE (Kelly & Knottenbelt, 2015), REDD (Kolter & Johnson, 2011), and REFIT (Murray et al., 2017) providing power measurements from multiple homes. |
| Dataset Splits | No | No explicit mention of a separate validation dataset split (percentage or count) was found in the paper. The paper specifies training and testing samples, for example: 'scenario A involved training on REFIT and testing on UK-DALE, 18.3k samples... the test set consisted of 3.5k samples'. |
| Hardware Specification | Yes | The experiments are performed on four NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation, such as 'Python 3.8, PyTorch 1.9, and CUDA 11.1'. |
| Experiment Setup | Yes | Based on the grid search, we found that DIOSC s best performance is obtained by (λ = 2.3, β = 1.5). The experiments are performed on four NVIDIA A100 GPUs. Hyperparameter settings are available in Appendix D. And, we set L = 16, and we fix an time window input to 256 steps, for the latent space dimension we fix dz = 16. |