Variational Autoencoders with Riemannian Brownian Motion Priors
Authors: Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Soren Hauberg
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For our first experiment we train a VAE with a Riemannian Brownian motion prior (R-VAE) for different latent dimensions and compare it to a VAE with a standard Normal prior and a VAE with a Vamp Prior. Tables 1 & 2 show the results. We next assess the usefulness of the latent representations of R-VAE. R-VAE has produced more separable clusters in the latent space... We quantitatively measured the utility of the R-VAE latent codes in different dimensionalities by training a classifier to predict digit labels and measuring the average overall and per-digit F1 score. Table 3 shows the results when comparing against the same classifier trained on latent codes derived by a VAE. |
| Researcher Affiliation | Academia | 1Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark 2Research Institutes of Sweden, Isafjordsgatan 22, 164 40 Kista, Sweden 3Empirical Inference Department, Max Planck Institute for Intelligent Systems, T ubingen, Germany. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that code is released or available. |
| Open Datasets | Yes | a VAE is trained on the 1-digits of MNIST (Page 2) and Results on Fashion MNIST (Table 2). |
| Dataset Splits | No | The paper mentions training models for a certain number of epochs and optimization strategies, but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The acknowledgements section states 'We gratefully acknowledge the support of the NVIDIA Corporation with the donation of GPU hardware.' However, it does not specify any particular GPU model or other hardware components. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer (Kingma & Ba, 2015)' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | All models were trained for 300 epochs. ...All experiments were run with the Adam optimizer (Kingma & Ba, 2015) with default parameter settings and a fixed learning rate of 10-3. The batch size was 100 for all models. |