Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
Authors: Giovanni De Felice, Andrea Cini, Daniele Zambon, Vladimir Gusev, Cesare Alippi
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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. |