Results 111 to 120 of about 1,718,101 (355)

Convexified Convolutional Neural Networks

CoRR, 2016
We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented as a low-rank matrix, which can be relaxed to ...
Yuchen Zhang 0002, Percy Liang, Martin J. Wainwright   +2 more
openaire   +3 more sources

Transferable Convolutional Neural Network for Weed Mapping With Multisensor Imagery

, 2021
Automatic weed monitoring and classification are critical for effective site-specific weed management. With the increasing availability of different sensors, it is possible for weed management to be achieved by processing a wide range of images captured ...
Hu, J, Zhou, J, Jia, X, Farooq, A
core   +1 more source

Quantifying Subsurface Weak in‐Plane Magnetization of Mixed Phase BiFeO3 by Scanning Nitrogen Vacancy Magnetometry

Advanced Functional Materials, EarlyView.
We use scanning nitrogen vacancy magnetometry to directly image the weak in‐plane magnetic moments in mixed phase BiFeO3 at the nanoscale and quantify the local magnetic moments to be 18.8±2.0 μB/nm2 in the rhombohedral‐like phase and 1.5±0.6 μB/nm2 in the well‐known non‐magnetic tetragonal‐like phase.
Lei Wang, Zhewen Xu, Yuwen Xu, Björn Josteinsson, Simon Josephy, Dawei Zhang, Yunlong Sun, Heung‐Sik Park, Jihun Kim, Kanghyun Chu, Kyung Song, Danyang Wang, Chan‐Ho Yang, Andrea Morales, Jan Seidel   +14 more
wiley   +1 more source

A Frequency-Domain Convolutional Neural Network Architecture Based on the Frequency-Domain Randomized Offset Rectified Linear Unit and Frequency-Domain Chunk Max Pooling Method

IEEE Access, 2020
It is of great importance to construct a convolutional neural network architecture in the frequency domain to explore the theory of deep learning in the frequency domain.
Jinhua Lin, Lin Ma, Jingxia Cui
doaj   +1 more source

INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks.
R. Niessner, H. Schilling, B. Jutzi
doaj   +1 more source

Development and Application of Convolutional Neural Network Model

Jisuanji kexue yu tansuo, 2021
Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field of rapid development in the past ten years, more and more researchers pay attention to it.
YAN Chunman ,WANG Cheng
doaj   +1 more source

Multimodal Perception and Machine Learning‐Empowered Human Machine Interfaces With Double‐Network Hydrogel Fibers

Advanced Functional Materials, EarlyView.
This work develops polyacrylamide‐alginate (PAM‐Alg) double‐network hydrogel fibers for multimodal perception and intelligent human‐machine interfaces. The covalent‐ionic network provides high strength, toughness, and stable conductivity. Easily woven into wearables and integrated with soft robots, the fibers enable object and temperature recognitions ...
Yujue Yang, Minyu Qiu, Di Tan, Xinlong Liu, Junze Zhang, Ziyu He, Jing Han, Yuanyuan Gao, Zihua Li, Hong Fu, Bingang Xu   +10 more
wiley   +1 more source

A classification method of underwater target radiated noise signals based on enhanced images and convolutional neural networks

Frontiers in Physics
As the economy and society continue to develop, the range of underwater vehicles is expanding and technology is constantly being upgraded. Consequently, it is becoming increasingly difficult to classify and identify them, and the traditional ...
Yuan Muye, Lei Zhufeng, Dang Yinuo, Shi Zanrong   +3 more
doaj   +1 more source

Bone tumor examination based on FCNN-4s and CRF fine segmentation fusion algorithm

Journal of Bone Oncology, 2023
Background and objective: Bone tumor is a kind of harmful orthopedic disease, there are benign and malignant points. Aiming at the problem that the accuracy of the existing machine learning algorithm for bone tumor image segmentation is not high, a bone ...
Shiqiang Wu, Xiaoming Bai, Liquan Cai, Liangming Wang, XiaoLu Zhang, Qingfeng Ke, Jianlong Huang   +6 more
doaj   +1 more source

Understanding Convolutional Neural Networks

CoRR, 2016
Statistical Machine Learning Course Project at Carnegie Mellon ...
openaire   +2 more sources