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..
Connectivity-Optimized Representation Learning via Persistent Homology
Authors: Christoph Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. One-class learning experiments ( 5) on large-scale vision data, showing that kernel-density based one-class models can be built on top of representations learned by a single autoencoder. These representations are transferable across datasets and, in a low sample size regime, our one-class models outperform recent stateof-the-art methods by a large margin. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Salzburg, Austria 2Microsoft 3UNC Chapel Hill. |
| Pseudocode | No | The paper includes mathematical definitions and theorems but no structured pseudocode or algorithm blocks with typical formatting or explicit labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | https://github.com/c-hofer/COREL_icml2019 |
| Open Datasets | Yes | CIFAR-10 (Krizhevsky & Hinton, 2009)... CIFAR-100... Tiny-Image Net... Image Net. For large-scale testing, we use the ILSVRC 2012 dataset (Deng et al., 2009) |
| Dataset Splits | Yes | CIFAR-10 (Krizhevsky & Hinton, 2009) contains 60,000 natural images of size 32 32 in 10 classes. 5,000 images/class are available for training, 1,000/class for validation. |
| Hardware Specification | Yes | On one GPU (Nvidia GTX 1080 Ti) this requires 75 hrs. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2017)' but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | The MLP is trained for 60 epochs with batch size 50 and η = 2. For optimization, we use Adam (Kingma & Ba, 2014) with a fixed learning rate of 0.001, (β1, β2) = (0.9, 0.999) and a batch-size of 100. The model is trained for 50 epochs. We fix η = 2 throughout our experiments. |