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..
Recurrent Existence Determination Through Policy Optimization
Authors: Baoxiang Wang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental analysis demonstrates significant efficiency and accuracy improvement over existing approaches, on both synthetic and real-world datasets. and RED is evaluated empirically on both synthetic datasets, Stained MNIST, and real-world datasets. |
| Researcher Affiliation | Academia | Baoxiang Wang The Chinese University of Hong Kong EMAIL |
| Pseudocode | No | The paper describes the algorithm using mathematical formulations and textual descriptions but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | RED is evaluated empirically on both synthetic datasets, Stained MNIST, and real-world datasets. Stained MNIST is a set of handwritten digits from MNIST. and We test and compare the performance using a dataset publicly available on Kaggle5. |
| Dataset Splits | No | The paper mentions a 'training subset' for hyperparameter search but does not provide specific details on dataset splits (training, validation, test percentages or counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes the models and algorithms used but does not provide specific software dependencies or version numbers (e.g., libraries, frameworks). |
| Experiment Setup | Yes | The hyper-parameters of RED are set to be c = 3, n1 = 18, n2 = 36, n3 = 54 for attention mechanism and γ = 0.95, k = 25, t0 = 10 for prediction aggregation, through a random search on a training subset. and The horizon is fixed to T = 350, where no significant improvement can be observed by further increasing it. |