4 papers
Impact of Disentanglement on Pruning Neural Networks
Carl Shneider, Peyman Rostami, Anis Kacem +3
Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption…
Novelty Driven Evolutionary Neural Architecture Search
Nilotpal Sinha, Kuan-Wen Chen
Evolutionary algorithms (EA) based neural architecture search (NAS) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. This can b…
Neural Architecture Search using Progressive Evolution
Nilotpal Sinha, Kuan-Wen Chen
Vanilla neural architecture search using evolutionary algorithms (EA) involves evaluating each architecture by training it from scratch, which is extremely time-consuming. This can…
Neural Architecture Search using Covariance Matrix Adaptation Evolution Strategy
Nilotpal Sinha, Kuan-Wen Chen
Evolution-based neural architecture search requires high computational resources, resulting in long search time. In this work, we propose a framework of applying the Covariance Mat…