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..
Ladder Capsule Network
Authors: Taewon Jeong, Youngmin Lee, Heeyoung Kim
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on MNIST demonstrate that the ladder capsule network learns an equivariant representation and improves the capability to extrapolate or generalize to pose variations. |
| Researcher Affiliation | Academia | 1Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea. Correspondence to: Heeyoung Kim <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Dynamic routing algorithm (Sabour et al., 2017) |
| Open Source Code | Yes | The code for L-Caps Net is available at https://github.com/taewonjeong/L-Caps Net. |
| Open Datasets | Yes | The experiments on MNIST demonstrate that the ladder capsule network learns an equivariant representation and improves the capability to extrapolate or generalize to pose variations. |
| Dataset Splits | No | The paper specifies training and testing set sizes (60,000 training, 10,000 testing for MNIST), but does not explicitly mention a separate validation set or specify train/validation/test split percentages beyond a training and testing split. |
| Hardware Specification | No | The paper does not explicitly state the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. It only mentions 'average computation time' without hardware context. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Re Lu activation function' but does not specify any software libraries or dependencies with their version numbers. |
| Experiment Setup | Yes | We trained the L-Caps Net with the margin loss with m+ = 0.9, m = 0.1, and λ = 0.5. In addition, we found that adding the loss of difference between the code vector and lower-level activity level, cl Al 2, would be helpful for training; thus we trained the L-Caps Net with the loss L = Lmargin + ϵ cl Al 2 with ϵ = 0.0001. We used the Adam optimizer with exponentially decaying learning rate starting from 0.001. |