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Found 5 papers, 4 papers with code
SMLP: Symbolic Machine Learning Prover
Symbolic Machine Learning Prover (SMLP) is a tool and a library for system exploration based on data samples obtained by simulating or executing the system on a number of input vectors.
LGEM$^\text{+}$: a first-order logic framework for automated improvement of metabolic network models through abduction
The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation.
Machine Learning Meets The Herbrand Universe
In particular, we develop a GNN2RNN architecture based on an invariant graph neural network (GNN) that learns from problems and their solutions independently of symbol names (addressing the abundance of skolems), combined with a recurrent neural network (RNN) that proposes for each clause its instantiations.
Bayesian Optimisation with Formal Guarantees
Application domains of Bayesian optimization include optimizing black-box functions or very complex functions.
Premise selection with neural networks and distributed representation of features
We present the problem of selecting relevant premises for a proof of a given statement.
