Neural architecture search (NAS) has become increasingly popular in the deep learning
community recently, mainly because it can provide an opportunity to allow interested users …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …

We propose a novel progressive parameter-sharing strategy (MPPS) in this paper for
effectively training multitask learning models on diverse computer vision tasks …

Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then
inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost …

Neural parameter allocation search (NPAS) automates parameter sharing by obtaining
weights for a network given an arbitrary, fixed parameter budget. Prior work has two major …

One-shot neural architecture search (NAS) substantially improves the search efficiency by
training one supernet to estimate the performance of every possible child architecture (ie …

Neural architecture search tries to shift the manual design of neural network (NN)
architectures to algorithmic design. In these cases, the NN architecture itself can be viewed …

Neural architecture search (NAS) is becoming increasingly popular for its ability to
automatically search for an appropriate network architecture, avoiding laborious manual …

Neural architecture search (NAS) can automatically discover well-performing architectures
in a large search space and has been shown to bring improvements to various applications …

While Chain of Thought (CoT) prompting approaches have significantly consolidated the
reasoning capabilities of large language models (LLMs), they still face limitations that …