Results 311 to 320 of about 14,062,463 (376)

An improved lightweight method based on EfficientNet for birdsong recognition. [PDF]

Sci Rep
He H, Luo H.
europepmc   +1 more source

Multimodal Deep Learning for Stage Classification of Head and Neck Cancer Using Masked Autoencoders and Vision Transformers with Attention-Based Fusion. [PDF]

Cancers (Basel)
Turki A, Alshabrawy O, Woo WL.
europepmc   +1 more source

Attention residual network for medical ultrasound image segmentation. [PDF]

Sci Rep
Liu H, Zhang P, Hu J, Huang Y, Zuo S, Li L, Liu M, She C.   +7 more
europepmc   +1 more source

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Attention to the Macroeconomy

SSRN Electronic Journal, 2023
Attention to the economy plays a key role in canonical macro models, yet its empirical properties are not well understood. We collect novel measures of attention to the economy based on open-ended survey questions. Our measures are included in tailored panel surveys of German firms and households, conducted before and during a large shock to inflation.
Link, Sebastian, Peichl, Andreas, Roth, Christopher, Wohlfart, Johannes   +3 more
openaire   +5 more sources

CBAM: Convolutional Block Attention Module

European Conference on Computer Vision, 2018
We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module sequentially infers attention maps along two separate dimensions,
Sanghyun Woo, Jongchan Park, Joon-Young Lee, In-So Kweon   +3 more
semanticscholar   +1 more source

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

European Conference on Computer Vision, 2018
Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train.
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Y. Fu   +5 more
semanticscholar   +1 more source

Graph Attention Networks

International Conference on Learning Representations, 2017
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their ...
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, P. Lio’, Yoshua Bengio   +5 more
semanticscholar   +1 more source

Attention deficit hyperactivity disorder.

British journal of hospital medicine, 2018
Attention deficit hyperactivity disorder is a highly heritable medical condition which mainly affects school-aged children although it is increasingly being recognized in adults.
Muhammed Ather, G. Salmon
semanticscholar   +1 more source