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..
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Authors: Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on a range of experiments focusing on dynamic representation learning and generative modeling, demonstrating the strong performance of our approach in comparison to existing methods that combine Gaussian Processes and autoencoders. |
| Researcher Affiliation | Academia | 1Department of Data Science, EURECOM, France 2Departments of Statistics and Computer Science, University of California, Irvine, USA. |
| Pseudocode | Yes | Algorithm 1: Inference for BAEs with SGHMC |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology described, nor does it include links to a code repository. |
| Open Datasets | Yes | We follow the data generation procedure of Jazbec et al. (2021), in which a squared-exponential GP kernel with a lengthscale l = 2 was used. Notice that, unlike Jazbec et al. (2021), we generate a fixed number of 35 videos for training and another 35 videos for testing... In the next experiment, we consider a large-scale benchmark of conditional generation...a rotated MNIST dataset (N = 4050). |
| Dataset Splits | Yes | Unlike Jazbec et al. (2021), we generate a fixed number of 35 videos for training and another 35 videos for testing. |
| Hardware Specification | Yes | All experiments were conducted on a server equipped with a Tesla T4 GPU having 16 GB RAM. |
| Software Dependencies | No | The paper mentions 'We use an Adam optimizer (Kingma & Ba, 2015)' but does not specify a version number for Adam or any other software libraries (e.g., Python, PyTorch/TensorFlow, scikit-learn) required to reproduce the experiments. |
| Experiment Setup | Yes | We set the hyperparameters of the number of SGHMC and optimization steps to J = 30, and K = 50, respectively. The details for all experiments are available in Appendix D. |