Search Results for author:
Found 5 papers, 3 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.
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.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
