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..
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
Authors: Hyunki Seong, Hyunchul Shim
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In realworld indoor environments, Mo Net demonstrates effective visual autonomous navigation, outperforming baseline models by 7% to 28% in task specificity analysis. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. Correspondence to: Hyunki Seong <EMAIL>. |
| Pseudocode | No | The paper includes equations and diagrams but does not feature any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | A demo video, codes, and dataset for quantitative results and interpretation are available at https://sites.google.com/view/monet-lgc. |
| Open Datasets | Yes | A demo video, codes, and dataset for quantitative results and interpretation are available at https://sites.google.com/view/monet-lgc. |
| Dataset Splits | Yes | The data is split into training and validation sets at a ratio of 80 : 20. |
| Hardware Specification | Yes | Our platform consists of a 1/10 scale racing car chassis (TT-02) equipped with an embedded computer (Jetson Xavier NX) and a controller (Arduino). |
| Software Dependencies | No | The paper mentions using "Adam optimizer" but does not specify a version number for the optimizer or any other software libraries/frameworks. |
| Experiment Setup | Yes | Batch size 512 Total training iterations 650k Optimizer Adam Similarity factor κ 0.5 Weight for the LGC loss term λLGC 5e-4 Learning rate 3e-4 Learning rate scheduler Lambda LR Scheduler factor 3e-4 |