Georgia Tech & Google Propose a Novel Discrete Variational Autoencoder for Automatically Improving Code Efficiency | Synced
In the new paper Learning to Improve Code Efficiency, a research team from the Georgia Institute of Technology and Google Research presents a novel discrete generative latent-variable model designe...
Source: Synced | AI Technology & Industry Review
In the new paper Learning to Improve Code Efficiency, a research team from the Georgia Institute of Technology and Google Research presents a novel discrete generative latent-variable model designed to help programmers identify more computationally efficient code variants, taking a step toward automating the process of code performance optimization.