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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Reinforced Continual Learning
Authors: Ju Xu, Zhanxing Zhu
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 13,621 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,525 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks. |
| Researcher Affiliation | Academia | Ju Xu Center for Data Science, Peking University Beijing, China EMAIL Zhanxing Zhu Center for Data Science, Peking University & Beijing Institute of Big Data Research (BIBDR) Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1 RCL for Continual Learning |
| Open Source Code | No | The paper does not provide a statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | Datasets (1) MNIST Permutations [4]. ... (3) Incremental CIFAR-100 [9]. |
| Dataset Splits | No | While the paper mentions the use of a 'validation dataset Vt' for reward calculation, it does not provide specific split sizes (e.g., percentages or sample counts) for this validation set, only for training and test sets. |
| Hardware Specification | Yes | We implemented all the experiments in Tensorfolw framework on GPU Tesla K80. |
| Software Dependencies | No | The paper mentions 'Tensorfolw framework' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | No | The paper mentions varying hyperparameters (e.g., α, learning rate η, number of epochs Te) but does not provide their specific values in the text for replication. |