Softened Symbol Grounding for Neuro-symbolic Systems
Authors: Zenan Li, Yuan Yao, Taolue Chen, Jingwei Xu, Chun Cao, Xiaoxing Ma, Jian L\"{u}
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with three representative neuro-symbolic learning tasks demonstrate that, owing to its superior symbol grounding capability, our framework successfully solves problems well beyond the frontier of the existing proposals. |
| Researcher Affiliation | Academia | Zenan Li1, Yuan Yao1, Taolue Chen2, Jingwei Xu1, Chun Cao1, Xiaoxing Ma1, Jian L u1 1State Key Lab of Novel Software Technology, Nanjing University, China 2Department of Computer Science, Birkbeck, University of London, UK |
| Pseudocode | Yes | Algorithm 1 Neural Symbolic Learning Procedure |
| Open Source Code | Yes | The code is available at https://github.com/Soft Wiser-group/Soften-Ne Sy-learning. |
| Open Datasets | Yes | We first evaluate our approach on the handwritten formula dataset provided by Li et al. (2020). We next evaluate our approach on a visual Sudoku classification task (Wang et al., 2019). We randomly generate 3K/1K graphs as training/test set through Network X (Hagberg et al., 2008). |
| Dataset Splits | No | No explicit mention of a validation set split or its size/percentage was found. The paper primarily discusses training and test sets. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The projection operator is specific to each task, and the corresponding inverse projection operator is implemented by the Z3 SMT solver (Moura & Bjørner, 2008). Through parallel computation (Joblib Development Team, 2020). No version numbers are given for Z3 or Joblib. |
| Experiment Setup | Yes | For all tasks, the batch size was set to 64. For all comparison methods and our Stage I algorithm, the number of epochs is fixed to 1,000. For our Stage II algorithm, the number of epochs is fixed at 30. We fix T = 10 in Alg. 1, i.e., conducting ten random walk steps before one gradient descent step. ...RL, MAPO, and SSL conducted the Adam algorithm with learning rate 5e-4. For our approaches, we used the SGD algorithm with learning rate 0.1 in Stage I, and the Adam algorithm with learning rate 1e-3. |