Neural networks for image recognition have evolved through extensive manual design from
simple chain-like models to structures with multiple wiring paths. The success of ResNets …

Over the past several years progress in designing better neural network architectures for
visual recognition has been substantial. To help sustain this rate of progress, in this work we …

The success of neural networks has driven a shift in focus from feature engineering to
architecture engineering. However, successful networks today are constructed using a small …

Neural architecture search (NAS) aims to automatically design deep neural networks of
satisfactory performance. Wherein, architecture performance predictor is critical to efficiently …

We introduce a parameter sharing scheme, in which different layers of a convolutional
neural network (CNN) are defined by a learned linear combination of parameter tensors …

Abstract Neural Architecture Search (NAS) yields state-of-the-art neural networks that
outperform their best manually-designed counterparts. However, previous NAS methods …

Designing accurate and efficient ConvNets for mobile devices is challenging because the
design space is combinatorially large. Due to this, previous neural architecture search (NAS) …

Neural Architecture Search (NAS) has been explosively studied to automate the discovery of
top-performer neural networks. Current works require heavy training of supernet or intensive …

The time and effort involved in hand-designing deep neural networks is immense. This has
prompted the development of Neural Architecture Search (NAS) techniques to automate this …

Can we automatically design a Convolutional Network (ConvNet) with the highest image
classification accuracy under the latency constraint of a mobile device? Neural architecture …