Recurrent Existence Determination Through Policy Optimization
Authors: Baoxiang Wang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 bxwang@cse.cuhk.edu.hk |
| 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. |