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..
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
Authors: Akinori F Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In tests on one original and two public video databases, Nosaic MNIST, UCF101, and Si W, the SPRT-TANDEM achieves statistically significantly better classification accuracy than other baseline classifiers, with a smaller number of data samples. 5 EXPERIMENTS AND RESULTS |
| Researcher Affiliation | Collaboration | 1NEC Corporation 2RIKEN Center for Advanced Intelligence Project (AIP) |
| Pseudocode | No | The paper describes the proposed algorithm in text and mathematical formulas but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and Nosaic MNIST are publicly available at https://github.com/Taiki Miyagawa/SPRT-TANDEM. |
| Open Datasets | Yes | Evaluated public databases are NMNIST, UCF, and Si W. ... The code and Nosaic MNIST are publicly available at https://github.com/Taiki Miyagawa/SPRT-TANDEM. ... UCF101 action recognition database (Soomro et al., 2012) and Spoofing in the Wild (Si W) database (Liu et al., 2018). |
| Dataset Splits | Yes | Training, validation, and test datasets are split and fixed throughout the experiment. ... The training, validation, and test datasets contain 50,000, 10,000, and 10,000 videos with frames of size 28 28 1 (gray scale). |
| Hardware Specification | Yes | All the experiments are conducted with custom python scripts running on NVIDIA Ge Force RTX 2080 Ti, GTX 1080 Ti, or GTX 1080 graphics card. |
| Software Dependencies | Yes | We use Tensorflow 2.0.0 (Abadi et al. (2015)) as a machine learning framework except when running baseline algorithms that are implemented with Py Torch (Paszke et al. (2019)). |
| Experiment Setup | Yes | Hyperparameters of all the models are optimized with Optuna unless otherwise noted so that no models are disadvantaged by choice of hyperparameters. See Appendix H for the search spaces and fixed final parameters. |