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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Variational Mixture of HyperGenerators for Learning Distributions over Functions
Authors: Batuhan Koyuncu, Pablo Sanchez Martin, Ignacio Peis, Pablo M. Olmos, Isabel Valera
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,328 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,340 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 | Through experiments on a diverse range of data types, such as images, voxels, and climate data, we show that Va Mo H can effectively learn rich distributions over continuous functions. |
| Researcher Affiliation | Academia | 1Saarland University, Saarbr ucken, Germany 2Max Planck Institute for Intelligent Systems, T ubingen, Germany 3Universidad Carlos III de Madrid, Madrid, Spain. |
| Pseudocode | Yes | Algorithm 1 Minibatch training of Va Mo H |
| Open Source Code | Yes | The code with the model implementation and experiments is available at https://github.com/bkoyuncu/vamoh. |
| Open Datasets | Yes | We evaluate Va Mo H on POLYMNIST (28 28), CELEBA HQ (64 64) (Karras et al., 2017), SHAPES3D (64 64) (Burgess & Kim, 2018), climate data from the ERA5 dataset (Hersbach et al., 2019), and 3D chair voxels from the SHAPENET dataset (Chang et al., 2015). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages or sample counts) needed to reproduce the experiment. |
| Hardware Specification | Yes | We implemented Va Mo H in Py Torch and performed all experiments on a single V100 with 32GB of RAM. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or versions for other key software dependencies or libraries. |
| Experiment Setup | Yes | Implementation details for Va Mo H are provided in Table 3. |