Search Results for author:
Found 8 papers, 3 papers with code
ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees
To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters.
Distributed, Parallel, and Cluster Computing
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload.
Performance Databases Distributed, Parallel, and Cluster Computing Software Engineering
ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application.
Multi-Label Graph Convolutional Network Representation Learning
The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning.
Ranked #30 on
Multi-Label Classification
on MS-COCO
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
By using spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task.
Evolutionary Architecture Search for Graph Neural Networks
In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.
Nonconvex ${L_ {1/2}} $-Regularized Nonlocal Self-similarity Denoiser for Compressive Sensing based CT Reconstruction
In this paper, we develop a ${{L_ {{1/2}}}} $-regularized nonlocal self-similarity (NSS) denoiser for CT reconstruction problem, which integrates low-rank approximation with group sparse coding (GSC) framework.
