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..
Connecting Sphere Manifolds Hierarchically for Regularization
Authors: Damien Scieur, Youngsung Kim
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimented the proposed method using five publicly available datasets, namely CIFAR100 (Krizhevsky, 2009), Caltech-UCSD Birds 200 (CUB200) (Welinder et al., 2010), Stanford-Cars (Cars) (Krause et al., 2013), Stanford-dogs (Dogs) (Khosla et al., 2011), and Tiny-Image Net (Tiny Im Net) (Deng et al., 2009). |
| Researcher Affiliation | Industry | 1Samsung SAIT AI Lab, Montreal 2Samsung Advanced Institute of Technology (SAIT). |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We experimented the proposed method using five publicly available datasets, namely CIFAR100 (Krizhevsky, 2009), Caltech-UCSD Birds 200 (CUB200) (Welinder et al., 2010), Stanford-Cars (Cars) (Krause et al., 2013), Stanford-dogs (Dogs) (Khosla et al., 2011), and Tiny-Image Net (Tiny Im Net) (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology for training, validation, and test sets). |
| Hardware Specification | Yes | All tests are conducted using NVIDIA Tesla V100 GPU with the same random seed. |
| Software Dependencies | No | The paper mentions using 'Geoopt optimizer (Kochurov et al., 2020)' but does not provide specific version numbers for software components or libraries. |
| Experiment Setup | Yes | We used the stochastic gradient descent (SGD) over 300 epochs, with a mini-batch of 64 and a momentum parameter of 0.9 for training. The learning rate schedule is the same for all experiments, starting at 0.1, then decaying by a factor of 10 after 150, then 225 epochs. |