Laplacian Autoencoders for Learning Stochastic Representations
Authors: Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz, Nicki Skafte, Søren Hauberg
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |