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..
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series
Authors: Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed model has undergone extensive simulation studies to evaluate its performance. |
| Researcher Affiliation | Academia | Adrian Atienza , Jakob Bardram , Sadasivan Puthusserypady Department of Health Technology, Technical University of Denmark EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | The model is trained with 10 second-length signals belonging to the Sleep Heart Health Study (SHHS) dataset [Zhang et al., 2018], [Quan et al., 1998]. ... All used databases are publicly available in Physionet [Goldberger et al., 2000] and National Sleep Research Resource (NSRR). |
| Dataset Splits | Yes | We conducted a five-fold cross-validation to evaluate the performance of the downstream tasks." and "We used the dataset s predefined partitioning of train and validation sets for evaluating the SVC model fitted on top of the representations." and "In the second, we have conducted a Leave-One Out (LOO) validation across the 23 MIT-AFIB subjects. |
| Hardware Specification | Yes | The training procedure and the evaluations are performed on a local computer, with a Nvidia Ge Force RTX 3070 GPU. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not specify versions for other software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | The input data is a time series of 1000 samples, which correspond to 10 seconds-length signal sampled at 100Hz. This input is split into segments of a length of 20 samples. The model counts with 6 regular transformer blocks with 4 heads each. The model dimension is set to 128... The projectors and predictors in our approach are implemented as a two-layer Multilayer Perceptron (MLP) with a dimensionality of 512 and 256... The EMA updating factor (τ) is set to 0.995. The window size is set to 2 minutes. We weigh the covariance loss with a factor of 0.1. We optimize the most important 32 features during the selective optimization. The training procedure consists of 30,000 iterations. We use a batch size of 256, and Adam... with a learning rate of 3e 4 and a weight decay of 1.5e 6 as the optimizer. |