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..
Embedding Symbolic Knowledge into Deep Networks
Authors: Yaqi Xie, Ziwei Xu, Mohan S. Kankanhalli, Kuldeep S Meel, Harold Soh
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our approach improves the performance of models trained to perform entailment checking and visual relation prediction. |
| Researcher Affiliation | Academia | Yaqi Xie , Ziwei Xu , Mohan S Kankanhalli, Kuldeep S. Meel, Harold Soh School of Computing National University of Singapore EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have made our source code available online at https://github.com/Ziwei XU/LENSR. |
| Open Datasets | Yes | We evaluated our method on VRD [28]. The VRD dataset contains 5,000 images with 100 object categories and 70 annotated predicates (relations). For each image, we sample pairs of objects and induce their spatial relations. |
| Dataset Splits | No | The paper does not provide specific training/validation/test split percentages or sample counts for the datasets used. |
| Hardware Specification | No | The paper mentions running experiments 'on a standard workstation' but does not specify any exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions tools and libraries such as 'python-sat [27]', 'c2d [17]', 'GLo Ve embeddings [29]', 'Res Net', and 'Adam [30]', but it does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | For this test, each LENSR model comprised 3 layers, with 50 hidden units per layer. LENSR produces 100dimension embedding for each input formula/assignment. The neural network used for classification is a 2-layer perceptron with 150 hidden units. We set m = 1.0 in Eqn. 3 and λr = 0.1 in Eqn. 4. [...] h is a MLP with 2 layers and 512 hidden units. [...] We optimized this objective using Adam [30] with learning rate 10 3. [...] λ = 0.1 is a trade-off factor. |