Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Graph Mixture Density Networks
Authors: Federico Errica, Davide Bacciu, Alessio Micheli
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,381 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,580 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on a new benchmark application that leverages random graphs for stochastic epidemic simulations. We show a significant improvement in the likelihood of epidemic outcomes when taking into account both multimodality and structure. The empirical analysis is complemented by two real-world regression tasks showing the effectiveness of our approach in modeling the output prediction uncertainty. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Pisa. Correspondence to: Federico Errica <EMAIL>, Davide Bacciu <EMAIL>, Alessio Micheli <EMAIL>. |
| Pseudocode | No | The paper describes the EM framework for training and provides mathematical equations, but it does not include a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | We publicly release large datasets of stochastic SIR simulations... https://github.com/diningphil/graph-mixture-density-networks |
| Open Datasets | Yes | We publicly release large datasets of stochastic SIR simulations... We simulated the well-known stochastic SIR epidemiological model on Barabasi-Albert graphs of size 100 (BA-100)... We also carry out simulations for Erdos-Renyi graphs (ER-100)... We will evaluate our model on the large chemical benchmarks alchemy_full (Chen et al., 2019) and ZINC_full (Irwin et al., 2012; Bresson & Laurent, 2019) |
| Dataset Splits | Yes | We assess the performance of different models using a holdout strategy for all datasets (80%/10%/10% split). ...in these final training runs we use early stopping on a validation set extracted from the training set (10% of the training data). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam Optimizer' and 'the GIN convolution... adapted from Xu et al. (2019)', but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | MDN: C {2,3,5}, hidden units per convolution {64}, neighborhood aggregation {sum}, graph readout {sum, mean}, α {1C, 1.05C}, epochs {2500}, Φi {Linear model}, Adam Optimizer with learning rate {0.0001}, full batch, patience {30}. GMDN: C {3,5}, graph convolutional layers {2,5,7}, hidden units per convolution {64}, neighborhood aggregation {sum}, graph readout {sum, mean}, α {1C, 1.05C}, epochs {2500}, Φi {Linear model}, Adam Optimizer with learning rate {0.0001}, full batch, patience {30}. |