Search Results for author:
Found 5 papers, 1 papers with code
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture.
Partial to Whole Knowledge Distillation: Progressive Distilling Decomposed Knowledge Boosts Student Better
Knowledge distillation field delicately designs various types of knowledge to shrink the performance gap between compact student and large-scale teacher.
Neural Architecture Search with Random Labels
In this paper, we investigate a new variant of neural architecture search (NAS) paradigm -- searching with random labels (RLNAS).
Structured Pruning of Recurrent Neural Networks through Neuron Selection
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications.
Learning to Search Efficient DenseNet with Layer-wise Pruning
Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
