Материалов:
1 005 012

Репозиториев:
30

Авторов:
596 024

По вашему запросу найдено документов: 13305

Страница 11 из 1331

Exploring convolutional neural networks and topic models for user profiling from drug reviews
Tutubalina E., Nikolenko S.
processing techniques, including extensions of topic models and convolutional neural networks (CNN). We apply
Discreteness, periodicity, holomorphy, and factorization
Vasilyev, V. B.
kind of discrete equations in certain canonical domains and holomorphy properties of their Fourier
On some equations on non-smooth manifolds: canonical domains and model operators
Vasilyev, V. B.
We describe different aspects of the theory of pseudo-differential equations on manifolds with non
Convolutional Neural Networks for the Segmentation of Multispectral Earth Remote Sensing Images
Vinogradov A.N., Tishchenko I.P., Ivanov E.S.
of Earth remote sensing using convolutional neural networks (CNN). Various modern algorithms
On the Classification of Cytological Images of Leukocytes Using Depthwise Separable Convolutional Neural Networks
Shchetinin E.Yu., Glushkova A.G., Demidova A.V., Sevastianov L.A.
images, LeucoCyteNetv2, is proposed, which includes depthwise separable convolutional layers, Separable
Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication
AbdelRaouf H., Chelloug S.A., Muthanna A., Semary N., Amin K., Ibrahim M.
an optimized convolutional neural network that is designed to extract improved features by utilizing data
Classes of Bianchi equations of third order
Mironov A.
On the basis of determining equations written out in terms of Laplace invariants, some classes
UNetX: Real-time Pedestrian Crosswalk Segmentation on Mobile Device
Adaska, E., Lechanka, A.
on an image in realtime. It is based on U-Net architecture with all its convolution layers substituted
ON TRANSFER LEARNING METHODS IN BIOMEDICAL IMAGES CLASSIFICATION TASKS [О МЕТОДАХ ПЕРЕНОСА ГЛУБОКОГО ОБУЧЕНИЯ В ЗАДАЧАХ КЛАССИФИКАЦИИ БИОМЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ]
Shchetinin E.Yu., Sevastianov L.A.
convolutional networks VGG-16, ResNet-50, Xception, and MobileNetV2 were used as the baseline models, pre
Geometric maximizers of Schatten norms of some convolution type integral operators
Ruzhansky M., Suragan D.
In this paper we prove that the ball is a maximizer of the Schatten p-norm of some convolution type

Страница 11 из 1331

Фасеты

Новости проекта

Новости

Партнеры

КФУ
ТГУ
Elpub
СФУ
Webpublishers

Индексация

BASE
WorldCat
OpenAIRE
EDS
core