Results 221 to 230 of about 504,050 (265)

Rolling bearing fault diagnosis in noisy environments using Channel-Time parallel attention networks. [PDF]

Sci Rep
Li G, Chen M, Lu Y, Zhang Y.
europepmc   +1 more source

Immunosuppressive Drug Use in Limited Systemic Sclerosis: An International Survey

Arthritis Care &Research, EarlyView.
Objective Current guidelines recommend immunosuppressive treatment for diffuse cutaneous systemic sclerosis but are less clear on their use in limited cutaneous systemic sclerosis (lcSSc) in the absence of internal organ complications. We conducted an international survey to understand current immunosuppressive drug prescribing patterns in lcSSc ...
Sabrina Hoa, Yicheng Mu, Marie Hudson, Janet Pope   +3 more
wiley   +1 more source

ArecaNet: Robust Facial Emotion Recognition via Assembled Residual Enhanced Cross-Attention Networks for Emotion-Aware Human-Computer Interaction. [PDF]

Sensors (Basel)
Kim J, Choi G.
europepmc   +1 more source

Multiscale deformed attention networks for white blood cell detection. [PDF]

Sci Rep
Zheng X, Xu Q, Zheng S, Zhao L, Liu D, Zhang L.   +5 more
europepmc   +1 more source

SCRIPT: Predicting Single-Cell Long-Range Cis-Regulation Based on Pretrained Graph Attention Networks. [PDF]

Adv Sci (Weinh)
Zhang Y, Wen B, Jiao Y, Liu Y, Guo X, Wu Y, Li J, Han L, Xu Y, Gao X, Qi Y, Cheng Y, He Y, Tian W.   +13 more
europepmc   +1 more source

ProtFun: a protein function prediction model using graph attention networks with a protein large language model. [PDF]

Bioinform Adv
Talo M, Bozdag S.
europepmc   +1 more source

A drug response prediction method for single-cell tumors combining attention networks and transfer learning. [PDF]

Front Med (Lausanne)
Zhou B, Sun S, Liu S, Long H, Li Y.
europepmc   +1 more source

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Attention in Attention Networks for Person Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
This paper generalizes the Attention in Attention (AiA) mechanism, in P. Fang et al., 2019 by employing explicit mapping in reproducing kernel Hilbert spaces to generate attention values of the input feature map. The AiA mechanism models the capacity of building inter-dependencies among the local and global features by the interaction of inner and ...
Pengfei Fang, Jieming Zhou, Soumava Kumar Roy, Pan Ji, Lars Petersson, Mehrtash Harandi   +5 more
openaire   +3 more sources

Attentional networks

Trends in Neurosciences, 1994
Recent brain-imaging and neurophysiological data indicate that attention is neither a property of a single brain area, nor of the entire brain. While attentional effects seem mediated by a relative amplification of blood flow and electrical activity in the cortical areas processing the attended computation, the details of how this is done through ...
M I, Posner, S, Dehaene
openaire   +2 more sources

Hypergraph Attention Networks

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020
Recently, graph neural networks have achieved great success on the representation learning of the graph-structured data. However, these networks just consider the pairwise connection between nodes which cannot model the complicated connections of data in the real world. Thus, researchers began to pay attention to the hypergraph modeling.
Chaofan Chen, Zelei Cheng, Zuotian Li, Manyi Wang   +3 more
openaire   +2 more sources