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..
Diffusion Variational Autoencoders
Authors: Luis A. Perez Rey, Vlado Menkovski, Jim Portegies
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that the VAE is indeed capable of capturing topological properties for datasets with a known underlying latent structure derived from generative processes such as rotations and translations. and 4 Experiments We have implemented 1 VAEs with latent spaces of ddimensional spheres, a ο¬at two-dimensional torus, a torus embedded in R3, the SO(3) and real projective spaces RPd . For all our experiments we used multi-layer perceptrons for the encoder and decoder with three and two hidden layers respectively. |
| Researcher Affiliation | Academia | Luis A. Perez Rey , Vlado Menkovski and Jim Portegies Eindhoven University of Technology, Eindhoven, The Netherlands |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | 1https://github.com/luis-armando-perez-rey/diffusion vae |
| Open Datasets | Yes | Mainly as a ο¬rst test of our algorithm, we trained VAEs on binarized MNIST [Salakhutdinov and Murray, 2008]. |
| Dataset Splits | No | The paper mentions training and test datasets but does not explicitly detail training/validation/test splits or specific percentages for validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) were provided for the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For all our experiments we used multi-layer perceptrons for the encoder and decoder with three and two hidden layers respectively. and We have set S = 10 throughout the presented results. and use an output layer with a tanh activation function to obtain 10 7 t 10 5. |