Variational Mixture of HyperGenerators for Learning Distributions over Functions
Authors: Batuhan Koyuncu, Pablo Sanchez Martin, Ignacio Peis, Pablo M. Olmos, Isabel Valera
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |