Paper Info
Title
FENAS - Flexible and Expressive Neural Architecture Search.
Abstract
Architecture search is the automatic process of designing the model or cell structure that is optimal for the given dataset or task. Recently, this approach has shown good improvements in terms of performance (tested on language modeling and image classification) with reasonable training speed using a weight sharing-based approach called Efficient Neural Architecture Search (ENAS). In this work, we propose a novel architecture search algorithm called Flexible and Expressible Neural Architecture Search (FENAS), with more flexible and expressible search space than ENAS, in terms of more activation functions, input edges, and atomic operations. Also, our FENAS approach is able to reproduce the well-known LSTM and GRU architectures (unlike ENAS), and is also able to initialize with them for finding architectures more efficiently. We explore this extended search space via evolutionary search and show that FENAS performs significantly better on several popular text classification tasks and performs similar to ENAS on standard language model benchmark. Further, we present ablations and analyses on our FENAS approach.
Year
DOI
Venue
2020
10.18653/V1/2020.FINDINGS-EMNLP.258
EMNLP
DocType
Volume
Citations 
Conference
2020.findings-emnlp
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Ramakanth Pasunuru1253.69
Mohit Bansal287163.19