Results 71 to 80 of about 75,461 (296)

Few-shot Neural Architecture Search

CoRR, 2020
Efficient evaluation of a network architecture drawn from a large search space remains a key challenge in Neural Architecture Search (NAS). Vanilla NAS evaluates each architecture by training from scratch, which gives the true performance but is extremely time-consuming. Recently, one-shot NAS substantially reduces the computation cost by training only
Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo 0001   +4 more
openaire   +3 more sources

Job-shop scheduling with an adaptive neural network and local search hybrid approach

, 2006
This article is posted here with permission from IEEE - Copyright @ 2006 IEEEJob-shop scheduling is one of the most difficult production scheduling problems in industry.
Yang, S, Shengxiang Yang, Yang, Shengxiang, Shengxiang Yang (7582940)   +3 more
core   +1 more source

Data Aware Neural Architecture Search

CoRR, 2023
Accepted as a full paper by the TinyML Research Symposium ...
Emil J. Njor, Jan Madsen, Xenofon Fafoutis   +2 more
openaire   +2 more sources

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Thrifty Neural Architecture Search for Medical Image Segmentation (Student Abstract)

, 2022
Convolutional neural network (CNN) based image segmentation has been widely used in analyzing medical images and benefited many real-world disease diagnosis applications.
Zhang, Miao, Chen, Ruibin, Pan, Shirui, Zhang, M, Zheng, Xin, Chen, R, Zheng, X, Pan, S   +7 more
core   +1 more source

Differentiable Neural Network Architecture Search

, 2023
The aim of this work is to propose a system for differentiable architecture search, which can be used for design of some neural network types. The work is based on the DARTS (Differentiable architecture search) approach and implements similar system in ...
Eichler, Vojtěch
core   +1 more source

Super‐Refractory Status Epilepticus (SRSE) in a Patient With Compound Heterozygous OPA1 Variants: Case Report and Literature Review

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Super‐Refractory Status Epilepticus (SRSE) is a rare, life‐threatening neurological emergency with unclear etiology in many cases. Mitochondrial dysfunction, often due to disease‐causing genetic variants, is increasingly recognized as a cause, with each gene producing distinct pathophysiological mechanisms.
Pouria Mohammadi, Lara Basovic, Christopher Michael McGraw   +2 more
wiley   +1 more source

BLOX : micro neural architecture search benchmark and algorithms [PDF]

, 2022
Neural architecture search (NAS) has been successfully used to design numerous high-performance neural networks. However, NAS is typically compute-intensive, so most existing approaches restrict the search to decide the operations and topological ...
Dudziak, Lukasz, Chau, Thomas, Wen, Hongkai, Lane, Nicholas D., Lane, Nicholas Donald, Dudziak, Łukasz, Abdelfattah, Mohamed S., Chau, Thomas C. P.   +7 more
core  

Training-free neural architecture search: A review

ICT Express
The goal of neural architecture search (NAS) is to either downsize the neural architecture and model of a deep neural network (DNN), adjust a neural architecture to improve its end result, or even speed up the whole training process.
Meng-Ting Wu, Chun-Wei Tsai
doaj   +1 more source

A Survey on Neural Architecture Search

CoRR, 2019
The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search. The choice of the network architecture has proven to be critical, and many advances in deep learning spring from its immediate improvements. However, deep learning
Martin Wistuba, Ambrish Rawat, Tejaswini Pedapati   +2 more
openaire   +2 more sources