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..
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
Authors: Giovanni De Felice, Andrea Cini, Daniele Zambon, Vladimir Gusev, Cesare Alippi
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we carry out an extensive empirical evaluation by exploring different use cases and assessing the performance of the proposed method against the state-of-the-art (Sec. 5). |
| Researcher Affiliation | Academia | Giovanni De Felice1 , Andrea Cini2, Daniele Zambon2, Vladimir V. Gusev1, Cesare Alippi2,3 1University of Liverpool 2The Swiss AI Lab IDSIA & Universit a della Svizzera italiana 3Politecnico di Milano |
| Pseudocode | No | The paper describes its methodology and architecture in detail but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Python code to reproduce the experiments is available online at https://github.com/ gdefe/ggnet-virtual-sensing. |
| Open Datasets | Yes | Complete climatic and photovoltaic datasets are publicly available online through the references provided in Appendix D. |
| Dataset Splits | Yes | Among all N D channels, we use 70% for training, 10% for validation, and the remaining 20% for testing. |
| Hardware Specification | Yes | Timings are taken on a machine equipped with an NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions 'Python' and 'Torch Spatiotemporal' but does not specify version numbers for these or any other software components. |
| Experiment Setup | Yes | For Gg Net and all baselines, we adopt the Adam optimizer (Kingma & Ba, 2015) with a learning rate lr = 0.001 paired with a cosine annealing learning rate scheduler. All models are trained for a maximum of 500 epochs, with a 30 epochs patience for early stopping. All batches have a size set to 32 and consider temporal windows of tw = 24 time steps. |