Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting
Authors: Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz3097-3104
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to validate our method, and show that our model learns and generalizes very well to large-scale #DNF instances. |
| Researcher Affiliation | Academia | Ralph Abboud, Ismail Ilkan Ceylan, Thomas Lukasiewicz Department of Computer Science University of Oxford, UK {ralph.abboud, ismail.ceylan, thomas.lukasiewicz}@cs.ox.ac.uk |
| Pseudocode | No | The paper describes a "Message Passing Protocol" with steps and provides a visual representation in Figure 1, but it does not contain structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper provides an arXiv link (arxiv.org/pdf/1904.02688.pdf) to an extended version of the paper, not a direct link to open-source code for the described methodology. There is no other statement regarding code availability. |
| Open Datasets | No | The paper states: "Owing to the lack of standardized benchmarks, we generate synthetic formulas using a novel randomized procedure designed to produce more variable formulas. We generate 100K distinct training formulas, where formula counts per n are shown in Table 1." While the generation procedure is described, the specific generated dataset files used are not made publicly available or linked. |
| Dataset Splits | No | The paper describes the generation of "100K distinct training formulas" and various "test set" formulas, but it does not explicitly mention or provide details for a separate validation dataset split. |
| Hardware Specification | Yes | We train the system for 4 epochs on a P100 GPU using KL divergence loss... KLM and the GNN ran over different hardware (Haswell E5-2640v3 CPU vs. P100 GPU, resp.). |
| Software Dependencies | No | The paper mentions using the "Adam optimizer" but does not provide specific version numbers for Adam or any other software libraries or frameworks used (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | For all experiments, we use k = 128-dimensional vector representations. We define fenc as a 3-layer MLP with layer sizes 8, 32, and 128, message-generating MLPs (Ml, Mc, and Md) as 4-layer MLPs with 128-sized layers, and fout as a 3-layer MLP with layers of size 32, 8, and 2... We train the system for 4 epochs on a P100 GPU using KL divergence loss, the Adam optimizer (Kingma and Ba 2015), a learning rate of λ = 10 5, a gradient clipping ratio of 0.5, and T = 8 message passing iterations. |