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Found 7 papers, 4 papers with code
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules
We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs.
Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models
We target the problem of provably computing the equivalence between two complex expression trees.
Neural Transfer Learning for Repairing Security Vulnerabilities in C Code
To sum up, this paper shows that transfer learning works well for repairing security vulnerabilities in C compared to learning on a small dataset.
Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model
In this work we target the problem of provably computing the equivalence between two programs represented as dataflow graphs.
Using Sequence-to-Sequence Learning for Repairing C Vulnerabilities
Software vulnerabilities affect all businesses and research is being done to avoid, detect or repair them.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning.
Synthetic Lung Nodule 3D Image Generation Using Autoencoders
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train.
