Search Results for author:
Found 7 papers, 2 papers with code
Transfer-Learning-Based Autotuning Using Gaussian Copula
We introduce the first generative TL-based autotuning approach based on the Gaussian copula (GC) to model the high-performing regions of the search space from prior data and then generate high-performing configurations for new tasks.
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging.
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization
Distributed data storage services tailored to specific applications have grown popular in the high-performance computing (HPC) community as a way to address I/O and storage challenges.
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations
The search space exposed by the transformation pragmas is a tree, wherein each node represents a specific combination of loop transformations that can be applied to the code resulting from the parent node's loop transformations.
Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples
In this study, we propose a new framework to solve inverse classification that maximizes the number of perturbed samples subject to a per-feature-budget limits and favorable classification classes of the perturbed samples.
Combined convolutional and recurrent neural networks for hierarchical classification of images
Deep learning models based on CNNs are predominantly used in image classification tasks.
Improved Classification Based on Deep Belief Networks
For better classification generative models are used to initialize the model and model features before training a classifier.
