Search Results for author:
Found 3 papers, 1 papers with code
Zero-shot Quantization: A Comprehensive Survey
Network quantization has proven to be a powerful approach to reduce the memory and computational demands of deep learning models for deployment on resource-constrained devices.
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
In this paper, we propose AugWard (Augmentation-Aware Training with Graph Distance and Consistency Regularization), a novel graph representation learning framework that carefully considers the diversity introduced by graph augmentation.
A Comprehensive Survey of Compression Algorithms for Language Models
How can we compress language models without sacrificing accuracy?
Cannot find the paper you are looking for? You can
Submit a new open access paper.
