This chapter argues for a novel method to machine learn patterns in formal proofs using statistical machine learning methods. The method exploits coalgebraic approach to proofs. The success of the method is demonstrated on three applications allowing to distinguish well-formed proofs from ill-formed proofs, identify families of proofs and even families of potentially provable goals.</p
Abstract. We propose a new method of feature extraction that allows to apply pattern-recognition abi...
We report the results of the first experiments with learning proof dependencies from the formalizati...
We develop kernels for measuring the similarity between relational instances using background knowle...
This chapter argues for a novel method to machine learn patterns in formal proofs using statistical ...
We present ML4PG - a machine learning extension for Proof General. It allows users to gather proof s...
We present ML4PG - a machine learning extension for Proof General. It allows users to gather proof s...
Abstract: We present ML4PG – a machine learning extension for Proof General. It allows users to gath...
The area of automata learning was pioneered by Angluin in the 80\u27s. Her original algorithm, which...
We present a novel technique for combining statistical machine learning for proof-pattern recognitio...
Recently, a growing number of researchers have applied machine learning to assist users of interacti...
The practice of mathematics involves discovering patterns and using these to formulate and prove con...
We propose a new method of feature extraction that allows to apply pattern-recognition abilities of ...
In this paper we present a framework for automated learning within mathematical reasoning systems. ...
This Documents is a Manual supporting the project Machine-learning coal-gebraic automated proofs. Se...
Across many fields of social science, machine learning (ML) algorithms are rapidly advancing researc...
Abstract. We propose a new method of feature extraction that allows to apply pattern-recognition abi...
We report the results of the first experiments with learning proof dependencies from the formalizati...
We develop kernels for measuring the similarity between relational instances using background knowle...
This chapter argues for a novel method to machine learn patterns in formal proofs using statistical ...
We present ML4PG - a machine learning extension for Proof General. It allows users to gather proof s...
We present ML4PG - a machine learning extension for Proof General. It allows users to gather proof s...
Abstract: We present ML4PG – a machine learning extension for Proof General. It allows users to gath...
The area of automata learning was pioneered by Angluin in the 80\u27s. Her original algorithm, which...
We present a novel technique for combining statistical machine learning for proof-pattern recognitio...
Recently, a growing number of researchers have applied machine learning to assist users of interacti...
The practice of mathematics involves discovering patterns and using these to formulate and prove con...
We propose a new method of feature extraction that allows to apply pattern-recognition abilities of ...
In this paper we present a framework for automated learning within mathematical reasoning systems. ...
This Documents is a Manual supporting the project Machine-learning coal-gebraic automated proofs. Se...
Across many fields of social science, machine learning (ML) algorithms are rapidly advancing researc...
Abstract. We propose a new method of feature extraction that allows to apply pattern-recognition abi...
We report the results of the first experiments with learning proof dependencies from the formalizati...
We develop kernels for measuring the similarity between relational instances using background knowle...
DOI
Add to ORKG