Results 61 to 70 of about 849,090 (345)

Hardware implementation of a convolutional neural network using calculations in the residue number system [PDF]

Компьютерная оптика, 2019
Modern convolutional neural networks architectures are very resource intensive which limits the possibilities for their wide practical application.
Nikolay Chervyakov, Pavel Lyakhov, Nikolay Nagornov, Maria Valueva, Georgiy Valuev   +4 more
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

Convolutional neural network-based onboard band selection for hyperspectral data with coarse band-to-band alignment [PDF]

Band selection is a key strategy to address the challenges of managing large hyperspectral datasets and reduce the dimensionality problem associated with the simultaneous analysis of hundreds of spectral bands.
Camps Carmona, Adriano José, del Prete, Roberto, Llaveria Godoy, David, Longepe, Nicolas, Meoni, Gabriele   +4 more
core   +1 more source

Toward a human‐centric co‐design methodology for AI detection of differences between planned and delivered dose in radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Introduction Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a misalignment between AI systems and their users.
Luca M. Heising, Frank Verhaegen, Stefan G. Scheib, Maria J. G. Jacobs, Carol X. J. Ou, Viola Mottarella, Yin‐Ho Chong, Mariangela Zamburlini, Sebastiaan M. J. J. G. Nijsten, Ans Swinnen, Michel Öllers, Cecile J. A. Wolfs   +11 more
wiley   +1 more source

Irregular Convolutional Neural Networks [PDF]

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), 2017
7 pages, 5 figures, 3 ...
Jiabin Ma, Wei Wang, Liang Wang
openaire   +2 more sources

Lecture Notes: Neural Network Architectures [PDF]

arXiv, 2023
These lecture notes provide an overview of Neural Network architectures from a mathematical point of view. Especially, Machine Learning with Neural Networks is seen as an optimization problem. Covered are an introduction to Neural Networks and the following architectures: Feedforward Neural Network, Convolutional Neural Network, ResNet, and Recurrent ...
arxiv  

Unsupervised non‐small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity‐based discriminator

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning‐based automatic segmentation methods rely on manually annotated data for network training. Purpose This study aims to develop an unsupervised tumor segmentation network smic‐GAN by using a similarity‐driven generative adversarial ...
Chengyijue Fang, Xiaoyang Li, Yidong Yang   +2 more
wiley   +1 more source

Phylogenetic convolutional neural networks in metagenomics [PDF]

BMC Bioinformatics, 2018
Presented at BMTL 2017 ...
Claudio Agostinelli, Valerio Maggio, Giuseppe Jurman, Cesare Furlanello, Ylenia Giarratano, Diego Fioravanti, Diego Fioravanti, Marco Chierici   +7 more
openaire   +7 more sources

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

, 2018
We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate the study of the larger 8x8 Ising model, showing that the deep neural ...
Mills, Kyle, Tamblyn, Isaac
core   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source