Search Results for author:
Found 6 papers, 0 papers with code
Learning-Based Automatic Synthesis of Software Code and Configuration
In this proposal, we set out to investigate this problem by breaking down automatic software generation and configuration into two different tasks.
ADA-GP: Accelerating DNN Training By Adaptive Gradient Prediction
Neural network training is inherently sequential where the layers finish the forward propagation in succession, followed by the calculation and back-propagation of gradients (based on a loss function) starting from the last layer.
Large Language Models Based Automatic Synthesis of Software Specifications
As software systems grow in complexity and scale, the number of configurations and associated specifications required to ensure the correct operation can become large and prohibitively difficult to manipulate manually.
Synthesizing Programs with Continuous Optimization
In this paper, we present a novel formulation of program synthesis as a continuous optimization problem and use a state-of-the-art evolutionary approach, known as Covariance Matrix Adaptation Evolution Strategy to solve the problem.
MERCURY: Accelerating DNN Training By Exploiting Input Similarity
If the Signature of a new input vector matches that of an already existing vector in the MCACHE, the two vectors are found to have similarities.
Learning Fitness Functions for Machine Programming
The problem of automatic software generation is known as Machine Programming.
