Neural Architecture Search

A Relation-Specific Attention Network for Joint Entity and Relation Extraction

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may …

One-Shot Neural Architecture Search via Novelty Driven Sampling

One-Shot Neural architecture search (NAS) has received wide attention due to its computational efficiency. Many One-Shot NAS methods use the validation accuracy based on the supernet as the stepping stone to search the best performing architecture …

Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization

One-Shot Neural Architecture Search (NAS) significantly improves the computational efficiency through weight sharing. However, this approach also introduces multi-model forgetting during the supernet training (architecture search phase), where the …