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 [1].
Compositional Neural Logic Programming
Authors: Son N. Tran
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we demonstrate the advantages of CNLP in discriminative tasks and generative tasks. and 4 Experiments section with subsections like 4.1 Comparison KB, 4.2 Addition, 4.3 Semantic Image Interpretation where performance metrics and comparisons are provided. |
| Researcher Affiliation | Academia | Son N. Tran University of Tasmania EMAIL |
| Pseudocode | Yes | Algorithm 1 Voting Backward-Forward Chaining (sketch) |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | 10000 digit images and their labels are extracted from the MNIST dataset1 as the facts for the digit predicate. and http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | We run 1000 queries for the addition(*,*,?) task using a computer with a quad-core 3.6 GHz CPU and 16 GB of RAM. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not provide specific ancillary software details like library or framework names with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | All models were trained by Adam optimizer with the batch size 64. and For a fair comparison, we also use the RMSProp optimiser for training as in [Donadello et al., 2017]. |