Results 21 to 30 of about 4,843,262 (245)

iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients [PDF]

, 2021
Differentiable ARchiTecture Search(DARTS) has recently become the mainstream in the neural architecture search (NAS) due to its efficiency and simplicity.
Zhang, Miao, Haffari, Reza, Abbasnejad, Ehsan, Su, Steven, Chang, Xiaojun, Pan, Shirui   +5 more
core  

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

FGATR-Net: Automatic Network Architecture Design for Fine-Grained Aircraft Type Recognition in Remote Sensing Images

Remote Sensing, 2020
Fine-grained aircraft type recognition in remote sensing images, aiming to distinguish different types of the same parent category aircraft, is quite a significant task.
Wei Liang, Jihao Li, Wenhui Diao, Xian Sun, Kun Fu, Yirong Wu   +5 more
doaj   +1 more source

Related Data for: You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms

, 2023
The related data for "You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms"
Luo, Xiangzhong, Liu, Weichen
core   +1 more source

FastDARTSDet: Fast Differentiable Architecture Joint Search on Backbone and FPN for Object Detection

Applied Sciences, 2022
Neural architecture search (NAS) is a popular branch of automatic machine learning (AutoML), which aims to search for efficient network structures.
Chunxian Wang, Xiaoxing Wang, Yiwen Wang, Shengchao Hu, Hongyang Chen, Xuehai Gu, Junchi Yan, Tao He   +7 more
doaj   +1 more source

Event Classification with Multi-step Machine Learning [PDF]

EPJ Web of Conferences, 2021
The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented ...
Saito Masahiko, Kishimoto Tomoe, Kaneta Yuya, Itoh Taichi, Umeda Yoshiaki, Tanaka Junichi, Iiyama Yutaro, Sawada Ryu, Terashi Koji   +8 more
doaj   +1 more source

Operation-level Progressive Differentiable Architecture Search

, 2021
Differentiable Neural Architecture Search (DARTS) is becoming more and more popular among Neural Architecture Search (NAS) methods because of its high search efficiency and low compute cost.
Jian Li, Wang, Weiping, Jun Liao, Zhu, Xunyu, Weiping Wang, Yong Liu, Xunyu Zhu, Liu, Yong, Li, Jian   +8 more
core   +1 more source

Improving Differentiable Architecture Search via Self-Distillation [PDF]

, 2023
Differentiable Architecture Search (DARTS) is a simple yet efficient Neural Architecture Search (NAS) method. During the search stage, DARTS trains a supernet by jointly optimizing architecture parameters and network parameters.
Wang, Weiping, Zhu, Xunyu, Liu, Yong, Li, Jian   +3 more
core   +1 more source

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

Complex & Intelligent Systems, 2023
Neural network pruning offers great prospects for facilitating the deployment of deep neural networks on computational resource limited devices.
Hanjing Cheng, Zidong Wang, Lifeng Ma, Zhihui Wei, Fawaz E. Alsaadi, Xiaohui Liu   +5 more
doaj   +1 more source

MOO-DNAS: Efficient Neural Network Design via Differentiable Architecture Search Based on Multi-Objective Optimization

IEEE Access, 2022
The progress devoted to improving the performance of neural networks has come at a high price in terms of cost and experience. Fortunately, the emergence of Neural Architecture Search improves the speed of network design, but most excellent works only ...
Hui Wei, Feifei Lee, Chunyan Hu, Qiu Chen   +3 more
doaj   +1 more source