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..
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
Authors: Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J Lim, Byoung-Tak Zhang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model in stream-based selective sampling scenarios with comparable query selection strategies, showing that MPART significantly outperforms competitive models. |
| Researcher Affiliation | Collaboration | 1AI Lab, CTO Division, LG Electronics, Seoul, Republic of Korea 2Seoul National University, Seoul, Republic of Korea 3University of Southern California, California, USA. |
| Pseudocode | Yes | Algorithm 1 The MPART algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | For experiments, we used four kinds of datasets with different distributions: Mouse retina transcriptomes (Macosko et al., 2015; Poliˇcar et al., 2019), Fashion MNIST (Xiao et al., 2017), EMNIST Letters (Cohen et al., 2017), and CIFAR-10 (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper mentions training data and a hold-out test dataset but does not specify a distinct validation set split or its details for the main model training. It mentions 30% of training data used for Parametric UMAP training, but this is for feature extraction, not for model validation in the main experiment. |
| Hardware Specification | No | The paper mentions "a 3.8 GHz CPU machine" but does not specify the CPU model, GPU, or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions "Python implementation" but does not specify exact version numbers for Python or any specific libraries (e.g., PyTorch, TensorFlow, scikit-learn) used. |
| Experiment Setup | Yes | The propagation rate δ for message passing was set to 0.1, and the parameters ke, τ and kd used for the score calculation were set to 1.0, 0.7 and 0.01, respectively. For other parameter settings, please refer to the Appendix. |