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Found 7 papers, 2 papers with code
OCTAL: Graph Representation Learning for LTL Model Checking
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification.
Graph Neural Networks for Reasoning 2-Quantified Boolean Formulas
In this paper, we target the QBF (Quantified Boolean Formula) satisfiability problem, the complexity of which is in-between propositional logic and predicate logic, and investigate the feasibility of learning GNN-based solvers and GNN-based heuristics for the cases with a universal-existential quantifier alternation (so-called 2QBF problems).
Graph Neural Reasoning for 2-Quantified Boolean Formula Solvers
In this paper, we investigate the feasibility of learning GNN (Graph Neural Network) based solvers and GNN-based heuristics for specified QBF (Quantified Boolean Formula) problems.
Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming
Training of deep learning models depends on gradient descent and end-to-end differentiation.
AutoGraph: Imperative-style Coding with Graph-based Performance
In machine learning, imperative style libraries like Autograd and PyTorch are easy to write, but suffer from high interpretive overhead and are not easily deployable in production or mobile settings.
Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator
Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay.
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
Despite the recent successes of deep neural networks in various fields such as image and speech recognition, natural language processing, and reinforcement learning, we still face big challenges in bringing the power of numeric optimization to symbolic reasoning.
