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..
Laplacian Autoencoders for Learning Stochastic Representations
Authors: Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz, Nicki Skafte, Søren Hauberg
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our Laplacian autoencoder estimates well-calibrated uncertainties in both latent and output space. We demonstrate that this results in improved performance across a multitude of downstream tasks. |
| Researcher Affiliation | Academia | Technical University of Denmark |
| Pseudocode | No | The paper includes a diagram (Figure 4: "Iterative training procedure") outlining the method, but it is not presented as structured pseudocode or an algorithm block formatted like code. |
| Open Source Code | Yes | The training code is implemented in Py Torch and available2. https://github.com/Frederik Warburg/Laplace AE |
| Open Datasets | Yes | We evaluate OOD performance on the commonly used benchmarks (Nalisnick et al., 2019b), where we use FASHIONMNIST (Xiao et al., 2017) as in-distribution and MNIST (Lecun et al., 1998) as OOD. ... In Tab. 4 we conduct a similar experiment on the CELEBA (Liu et al., 2015) facial dataset... |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See appendix. |
| Hardware Specification | Yes | The exact diagonal approximation run out of memory for an 36 × 36 × 3 image on a 11 Gb NVIDIA Ge Force GTX 1080 Ti. |
| Software Dependencies | No | The paper mentions "Py Torch" as the implementation framework but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | Appendix A provides more details on the experimental setup. Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See appendix. |