Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
Authors: Alessandro Daniele, Tommaso Campari, Sagar Malhotra, Luciano Serafini
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental analysis to substantiate the efficacy of DSL in simultaneously learning perception and symbolic functions. and We evaluate our approach on a set of tasks where a combination of perception and reasoning is essential. in Section 5. |
| Researcher Affiliation | Academia | Alessandro Daniele1 , Tommaso Campari1, 2 , Sagar Malhotra1, 3 and Luciano Serafini1 1Fondazione Bruno Kessler, Trento, Italy, 2Universit a degli Studi di Padova, Italy 3TU Wien, Austria |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks. The methodology is described in prose and through architectural diagrams (Figure 1 and Figure 2). |
| Open Source Code | Yes | 1Project webpage: https://dkm-fbk.github.io/projects/dsl.html |
| Open Datasets | Yes | Firstly, we test our system on a variant of the MNIST [Le Cun et al., 1998] Sum task proposed in [Manhaeve et al., 2018] |
| Dataset Splits | No | The paper mentions a 'training set' and 'test set' for its experiments (e.g., 'we trained our model on the 2-digits sum and we evaluate the learned model on sequences of varying length'), and also mentions using 'optuna to select the best hyperparameters', which implies the use of a validation set. However, it does not provide specific details on the dataset splits (e.g., percentages, sample counts for train/validation/test) or reference a standard predefined split with a citation for all experiments, except partially for specific tasks (e.g., 'sequences of 4 images during training and 20 on the test' for Visual Parity). |
| Hardware Specification | Yes | All the experiments were conducted with a machine equipped with an NVIDIA GTX 3070 with 12GB RAM. |
| Software Dependencies | No | The paper mentions 'Mad Grad [Defazio and Jelassi, 2022] for optimization and optuna to select the best hyperparameters'. However, it does not provide specific version numbers for these or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | To train the model we use the binary cross entropy loss on the confidence t of the predicted symbol. and In our experiments, we use ϵ-greedy during training, and greedy policy at test time. and We used Mad Grad [Defazio and Jelassi, 2022] for optimization and optuna to select the best hyperparameters for every experiment. and DSL reached an accuracy of 98.7 0.4 in 1000 epochs |