Scalable Coupling of Deep Learning with Logical Reasoning
Authors: Marianne Defresne, Sophie Barbe, Thomas Schiex
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show it is able to efficiently learn how to solve NP-hard reasoning problems from natural inputs as the symbolic, visual or many-solutions Sudoku problems as well as the energy optimization formulation of the protein design problem, providing data efficiency, interpretability, and a posteriori control over predictions. We test our architecture on logical (feasibility) problems with one or many solutions [Nandwani et al., 2021], ω being purely symbolic or containing images. We also apply it to a real, purely data-defined, discrete optimization problem to check the ability of the E-NPLL to estimate a criteria. |
| Researcher Affiliation | Academia | Marianne Defresne1,2 , Sophie Barbe2 and Thomas Schiex1 1Universit e F ed erale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France 2TBI, Universit e de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France |
| Pseudocode | No | The paper describes algorithmic steps in prose but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is written in Python using Py Torch version 11.10.2 and Py Toulbar2 version 0.0.0.2. Code and data are available at https://forgemia.inra.fr/marianne.defresne/emmental-pll. |
| Open Datasets | Yes | We use an existing data set [Palm et al., 2018], composed of single-solution grids with 17 to 34 hints. Our data set is obtained from the symbolic Sudoku data set by replacing hints with corresponding MNIST images, as in [Brouard et al., 2020]. For training, we use the data set of [Ingraham et al., 2019], already split into train/validation/test sets of respectively 17, 000, 600 and 1, 200 proteins, in such a way that proteins with similar structures or sequences are in the same set. |
| Dataset Splits | Yes | We use 1, 000 grids for training, and 256 for validation (all hardness). We use 1, 000, 64 and 256 grids of the data set from [Nandwani et al., 2021] respectively for training, validating, and testing. |
| Hardware Specification | Yes | Unless specified otherwise, all experiments use a Nvidia RTX-6000 with 24GB of VRAM and a 2.2 GHz CPU with 128 GB of RAM. |
| Software Dependencies | Yes | Our code is written in Python using Py Torch version 11.10.2 and Py Toulbar2 version 0.0.0.2. |
| Experiment Setup | Yes | We use the Adam optimizer with a weight decay of 10 4 and a learning rate of 10 3 (other parameters take default values). An L1 regularization with multiplier 2.10 4 is applied on the cost matrices N(ω)[i, j]. For N, we use a Multi-Layer Perceptron (MLP) with 10 hidden layers of 128 neurons and residual connections [He et al., 2016] every 2 layers. |