A General-Purpose Transferable Predictor for Neural Architecture Search

no code implementations • 21 Feb 2023 • Fred X. Han, Keith G. Mills, Fabian Chudak, Parsa Riahi, Mohammad Salameh, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu

In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.

Contrastive Learning Graph Representation Learning +1

A Transferable General-Purpose Predictor for Neural Architecture Search

no code implementations • 29 Sep 2021 • Fred X. Han, Fabian Chudak, Keith G Mills, Mohammad Salameh, Parsa Riahi, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu

Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).

Contrastive Learning Graph Representation Learning +1