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..
Topologically Densified Distributions
Authors: Christoph Hofer, Florian Graf, Marc Niethammer, Roland Kwitt
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we provide empirical evidence (across various vision benchmarks) to support our claim for better generalization. For our experiments, we draw on a setup common to many works in semi-supervised learning (...) In particular, we present experiments on three (10 class) vision benchmark datasets: MNIST, SVHN and CIFAR10. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Univ. of Salzburg, Austria 2Univ. of North Carolina, Chapel Hill, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Py Torch source code is available at https://github.com/c-hofer/topologically_densified_distributions |
| Open Datasets | Yes | In particular, we present experiments on three (10 class) vision benchmark datasets: MNIST, SVHN and CIFAR10. |
| Dataset Splits | Yes | Thus, we study the behavior of crossvalidating β, when the validation set is of size equal to the training corpus. The shaded region shows the variation in the testing error on small-validation sets. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch source code' but does not specify a version number or list other software dependencies with their versions. |
| Experiment Setup | Yes | Optimization is done by SGD with momentum (0.9) over 310 epochs with cross-entropy loss and cosine learning rate annealing (Loshchilov & Hutter, 2017) (without restarts). As all experiments use weight decay, it is important to note that batch normalization combined with weight decay mainly affects the effective learning rate (van Laarhoven, 2017; Zhang et al., 2019). (...) We choose a sub-batch size of b = 16 and draw n = 8 sub-batches (see 2.4); this amounts to a total batch size of 128. |