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..
Geometric Autoencoders - What You See is What You Decode
Authors: Philipp Nazari, Sebastian Damrich, Fred A Hamprecht
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments 5.1. Experimental Setup 5.2. Evaluation |
| Researcher Affiliation | Academia | 1HCI/IWR at University of Heidelberg, 69120 Heidelberg, Germany 2University of T ubingen, 72074 T ubingen, Germany. |
| Pseudocode | Yes | Algorithm 1 Calculating the Generalized Jacobian Determinant |
| Open Source Code | Yes | We provide the code as an open-source package for Py Torch. It can be found at https://github.com/hci-unihd/ Geometric Autoencoder. |
| Open Datasets | Yes | Datasets Besides the classical image datasets MNIST (Le Cun et al., 1998) and Fashion MNIST (Xiao et al., 2017), we use the three single-cell datasets Zilionis (Zilionis et al., 2019), CElegans (Packer et al., 2019) and PBMC (Zheng et al., 2017). |
| Dataset Splits | No | The paper does not explicitly mention a validation dataset split or a methodology for using one during training. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch', 'Geomstats package', and 'functorch library' but does not specify their version numbers. |
| Experiment Setup | Yes | All of the autoencoders except for the UMAP autoencoder are optimized using ADAM (Kingma & Ba, 2015), and trained using a batch size of 125, learning rate 10 3 and a weight decay of 10 5. ... The vanilla, topological and geometric autoencoders are trained for 100 epochs. For the proposed geometric autoencoder, we found α = 0.1 to be a good weight for the geometric loss term. |